Genetic Implications of Culturing Australasian Snapper (Chrysophrys Auratus) from Wild-Caught Eggs for Stock Enhancement

Genetic implications of culturing Australasian snapper (Chrysophrys auratus) from wild-caught eggs for stock enhancement

Natasha Danielle Prokop BSc (Marine Science); BComm (Journalism)

2015 Murdoch University, Western Australia School of Veterinary and Life Sciences

With support from

Declaration

I declare that this thesis is my account of my research and contains, as its main content, work that has not previously been submitted for a degree at any tertiary education institution.

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Natasha Prokop Date

Abstract

This thesis represents the first study into the genetic implications of culturing a marine finfish from wild-caught eggs (the ACAAR technique), for use in stock enhancement. This technique should facilitate the capture of genetic diversity in the cultured fish, while also dispensing with the need to maintain a large broodstock in the hatchery. This study was conducted with

Chrysophrys auratus from Cockburn Sound in Western Australia. Nine, polymorphic microsatellite loci were used to compare the genetic composition between a sample of wild adults of C. auratus from spawning aggregations in Cockburn Sound and a sample of juveniles cultured using the ACAAR technique.

The levels of genetic diversity present in the wild sample, in terms of allelic richness and heterozygosity, were maintained in the juveniles cultured using this technique and their release would likely pose little risk to genetic diversity in the wild. Overall, the allelic compositions were similar between the wild adult and cultured juveniles samples, although some rare alleles were absent in each population due to sampling bias, and bottleneck effects in the cultured juveniles. The levels of inbreeding and relatedness detected in the cultured juveniles were low, and similar to that of the wild adult sample, suggesting that juvenile releases would be unlikely to increase levels of inbreeding in the wild. It is also likely that a large number of effective breeders ( > 133) contributed to the cultured juveniles, thereby lowering the risk of inbreeding, or reductions to the wild effective population size, upon release. These findings together suggest that the ACAAR technique poses a limited risk to the genetic integrity of the wild population, should these fish be released for stock enhancement. It may also serve as a suitable alternative to traditional culturing for other aggregate spawning marine finfish. Further, suggestions are made for the improvement of the ACAAR culturing technique and for improvements to the genetic analyses.

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Table of contents

Declaration ...... ii Abstract ...... iii Table of contents ...... v List of Figures ...... viii List of Tables ...... x Acknowledgements ...... xii

Chapter One – Introduction ...... 1 1.1 Context of introduction ...... 1 1.2 Active fisheries management ...... 1 1.2.1 Stock enhancement for the management of fisheries...... 2 1.3 Genetic effects of stock enhancement ...... 4 1.3.1 Tools for assessing the genetic effects of enhancements ...... 5 1.3.1.1 Microsatellites ...... 5 1.3.2 Potential genetic effects of traditional stock enhancement ...... 7 1.3.2.1 Effects during culturing ...... 9 1.3.2.2 Effects in the wild ...... 12 1.3.3 The salmonid bias and marine enhancements ...... 14 1.3.4 Managing genetic effects ...... 18 1.3.5 Novel techniques ...... 20 1.4 This study ...... 21 1.4.1 Project overview ...... 21 1.4.2 Study species general biology ...... 23 1.4.2.1 Taxonomy ...... 23 1.4.2.2 Distribution ...... 24 1.4.2.3 Reproduction and recruitment ...... 24 1.4.3 Study species threats and management ...... 27 1.4.4 Motivation for enhancement ...... 29 1.4.4.1 Practicality ...... 29 1.4.4.1.1 Egg collection ...... 29 1.4.4.1.2 Culture technologies...... 30 1.4.4.1.3 Funding ...... 30 1.4.4.2 Responsible enhancement ...... 31 1.4.4.2.1 Recruitment limitation ...... 31 1.4.4.2.2 Genetic implications...... 31 1.4.5 Significance and need for this study ...... 32

1.4.5 Study objectives ...... 33

Chapter Two – Methods ...... 34 2.1 Sampling design and collection ...... 34 2.1.1 Sample site ...... 34 2.1.2 Sampling design ...... 35 2.1.2.1 Wild adults (2014) ...... 36 2.1.2.2 Cultured juveniles ...... 36 2.1.2.3 Wild adults (2013) ...... 39 2.2 DNA extraction ...... 40 2.2.1 Screening of nuclear DNA ...... 41 2.3 Polymerase chain reaction (PCR) ...... 42 2.3.1 Microsatellite loci ...... 42 2.3.2 PCR amplification ...... 43 2.3.3 Screening of PCR product ...... 44 2.3.4 Fragment length analysis ...... 44 2.4 Data analyses ...... 46 2.4.1 Locus selection ...... 46 2.4.1.1 Hardy-Weinberg equilibrium ...... 46 2.4.1.2 Null alleles ...... 47 2.4.1.3 Linkage disequilibrium ...... 48 2.4.2 Reliability of genotype scoring ...... 48 2.4.2.1 Fragment length analysis ...... 48 2.4.2.2 Sample size ...... 48 2.4.3 Genetic diversity per locus ...... 49 2.4.4 Comparison of genetic composition between cultured juveniles and wild adults ...... 49 2.4.5 Bottleneck...... 51 2.4.6 Inbreeding and relatedness ...... 52 2.4.7 Effective population size ...... 52

Chapter Three – Results ...... 54 3.1 Locus selection ...... 54 3.1.2 Hardy-Weinberg Equilibrium ...... 54 3.1.2 Null alleles ...... 54 3.2.3 Linkage Disequilibrium ...... 56 3.2 Reliability of genotype scoring ...... 58 3.2.1 Fragment length analysis and sample size ...... 58 3.3 Genetic diversity per locus ...... 58 3.4 Comparisons of genetic composition between cultured juveniles and wild adults ...... 62

3.5 Bottleneck ...... 65 3.6 Inbreeding and relatedness ...... 66 3.7 Effective population size ...... 67

Chapter Four – Discussion ...... 69 4.1 Overview ...... 69 4.2 Locus Selection ...... 69 4.3 Reliability of genotype scoring ...... 70 4.3.1 Quality and quantity of DNA ...... 70 4.3.2 Contamination with non-target DNA ...... 71 4.3.3 Inconsistent PCR amplification ...... 71 4.3.4 Fragment length analysis ...... 72 4.4 Genetic diversity per locus ...... 72 4.5 Genetic implications for stock enhancement ...... 73 4.5.1 Genetic diversity ...... 73 4.5.2 Allelic composition ...... 75 4.5.3 Inbreeding and relatedness ...... 78 4.5.4 Effective population size ...... 79 4.5.4.1 Reliability of estimates ...... 80 4.5.4.2 Implications of effective number of breeders ...... 82 4.6 Broader applications ...... 84 4.7 Further research ...... 85 4.7.1 Recommendations for culturing ...... 85 4.7.1.1 Pooling eggs ...... 85 4.7.2 Limitations and recommendations for genetic analyses ...... 85 4.7.2.1 Detection of genetic variability ...... 85 4.7.2.2 Estimates of effective population size ...... 86 4.7.2.1.2 Number of loci sampled ...... 86

4.7.2.1.3 Precise NeI estimates ...... 87

4.7.2.1.4 Comparable NbI estimates ...... 87 4.7.2.3 Impacts on other areas ...... 87 4.6 Final remarks ...... 88

References ...... 89

Appendix 1 – Raw genotype data ...... 109

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List of Figures

Figure 1.1: Summary of potential genetic effects that can occur at each stage of the culturing and enhancement process...... 8

Figure 1.2: Comparison of methods for traditional culturing for stock enhancement and the ACAAR technique for culturing Chrysophrys auratus from fertilised wild eggs collected from spawning aggregations in Cockburn Sound, Western Australia...... 22

Figure 1.3: Map of Western Australia showing the boundaries of the Department of Fisheries, Western Australia bioregional management zones, including the Gascoyne Coast (GCB), West Coast (WCB) and South Coast (SCB) bioregions (insert) and the within-bioregion management zones for the WCB: the Kalbarri, Mid-West, Metropolitan and South-west management areas. The Western Australian distribution of Chrysophrys auratus is indicated by the broken line ( ) in the insert. Source: (Fairclough et al., 2013) ...... 27

Figure 2.1: Location of the sampling site Cockburn Sound, a shallow marine embayment, in Western Australia. Depth contours of 0–10 m ( ) and >10 m ( ) are shown for coastal waters. Adapted from: Wakefield (2010)...... 35

Figure 2.2: Images of typical agarose gels from samples of a) wild adults, b) cultured juveniles and c) muscle tissue of Chrysophrys auratus from Cockburn Sound. Lanes 1-6 contain template DNA, lane 7 contains the negative control and lane 8 contains lambda DNA standard. Saturated pixels are indicated in red ...... 42

Figure 2.3: Boxplot distributions of observations for wild adults and cultured juveniles of Chrysophrys auratus from Cockburn Sound for a) the inbreeding coefficient, FIS and b) expected heterozygosity, HE ...... 50

Figure 3.1: Results from MICRO-CHECKER, indicating the expected homozygote allele frequencies (•) and 95% confidence intervals and observed homozygote frequencies (x) for the loci a) PauA119 and b) PauD118, with wild adults of Chrysophrys auratus from Cockburn Sound ...... 55

Figure 3.2: Rarefaction curves produced using the jackmsatpop function in the R statistics package, PopGenKit showing the relationship between sample size (K) and number of alleles detected (A) for a) wild adults and b) cultured juveniles of Chrysophrys auratus from Cockburn Sound, using an interval of one and 1,000 jackknifing repeats per sampling interval ...... 59

Figure 3.3: Composition and frequency (indicated by bubble size) of alleles for wild adults and cultured juveniles of Chrysophrys auratus from Cockburn Sound at the loci a) PauD116 and b) PMA1, which exhibited significant genic differentiation prior to Holm-Bonferroni correction ...... 64

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Figure 3.4: Histogram of allele frequencies produced by the program BOTTLENECK for nine loci with the wild adult and cultured juvenile samples of Chrysophrys auratus from Cockburn Sound ...... 65

Figure 3.5a) Mean of the posterior probabilities for unrelated relationships ± standard error and b) mean relatedness (r) ± standard error, produced in the program ML-RELATE, between wild adult and cultured juvenile samples of Chrysophrys auratus from Cockburn Sound ... 67

List of Tables

Table 1.1: Summary of results of genetic studies over the past 50 years that have compared hatchery and wild samples in terms of fitness and genetic diversity and which were reported in Araki & Schmid (2010)...... 11

Table 1.2: Summary of enhancement history, broodstock management (including % parental contributions) and genetic effects of marine finfish enhancements, for which genetic effects have been compared between cultured and wild populations, and for which long-term ( >15 years) commercial-scale enhancements have taken place...... 16

Table 1.3: Summary of scientific names that have been attributed to Chrysophrys auratus and have received attention in scientific debate over the past 50 years regarding its taxonomy ...... 23

Table 2.1: Characteristics of all 11 microsatellite loci used with Chrysophrys auratus from Cockburn Sound in this study including, for each locus, the GeneBank accession number, locus name, forward (F) and reverse (R) primer sequences and repeat motif. The A + sign indicates loci for which the poly-A tail (non-template adenine nucleotide base) was preferentially amplified and scored, rather than the true allele ...... 43

Table 2.2: Combinations of locus pairs loaded into the same wells of Fisher Biotec 96-well plates for capillary electrophoresis ...... 45

Table 3.1: Null allele frequencies for the loci PauA119 and PauD118, determined by MICRO- CHECKER using the algorithms of Van Oosterhout et al. (2004), Chakraborty et al. (1992) Brookfield (1996), and using the null allele frequency estimator in ML-RELATE (Kalinowski et al., 2006). The mean estimate of null allele frequencies for all four algorithms (± standard error) is also indicated ...... 56

Table 3.2: Results of pairwise G-tests for linkage disequilibrium between nine loci in samples of wild adults and cultured juveniles of Chrysophrys auratus from Cockburn Sound. P-values of the G-tests denote the probability of a Type I error for the null hypothesis that the diploid genotypes at one locus are independent of those at the other locus in the pair being tested. Results of global tests (for both samples combined) are exact probabilities from a chi‐square test (χ2) and associated degrees of freedom (df) using Fisher’s combined probability method . 57

Table 3.3: Locus characteristics for wild adults and cultured juveniles of Chrysophrys auratus from Cockburn Sound. Where N is the number of individuals sampled at each locus, A is the observed number of alleles, AR is the allelic richness, which represents the number of observed alleles independent of sample size, HE is the unbiased expected heterozygosity, HO is observed heterozygosity, FIS is Wright’s inbreeding coefficient and P is the resultant P-value from exact tests for Hardy-Weinberg equilibrium. The P-value of G test denotes exact probability of a Type I error for the null hypothesis that the allelic compositions and their frequencies at each locus in wild adult and cultured juvenile samples are the same ...... 60

Table 3.4: One-tailed (cultured juveniles < wild adults) P-values from nonparametric sign tests for observed number of alleles (A), allelic richness (AR) and unbiased expected heterozygosity (HE) and a one-tailed (cultured juveniles < wild adults) Wilcoxon signed-rank test for FIS, between the wild adult and cultured juvenile samples of Chrysophrys auratus from Cockburn Sound ...... 63

Table 3.5: Unique alleles detected in the wild adult and cultured juvenile samples of Chrysophrys auratus from Cockburn Sound, including the mean number of private alleles (± standard deviation) across all loci ...... 64

Table 3.6: P-values from sign tests for heterozygote excess, produced by the program BOTTLENECK, for wild adults and cultured juveniles of Chrysophrys auratus from Cockburn Sound, under three models of microsatellite mutation: the infinite alleles model (IAM), the step-wise mutation model (SMM) and two-phase mutation model (TPM) ...... 65

Table 3.7: Results, from the program ML-RELATE, of relationship and relatedness tests between wild adult and cultured juveniles of Chrysophrys auratus from Cockburn Sound, in terms of proportions of unrelated pairs (U), combined proportions of half- and full-sib pairs (HS/FS), mean of the posterior probabilities of unrelated pairs ± standard error and mean relatedness (r), ± standard error ...... 66

Table 3.8: Estimates of the inbreeding effective population size obtained for wild adults (inbreeding effective population size, NeI) and cultured juveniles (inbreeding effective number of breeders, NbI) of Chrysophrys auratus from Cockburn Sound using: i) LDNe (point estimates) and associated 95% jackknife confidence intervals and, ii) ONeSAMP (mean estimates), and associated 95% confidence intervals for different priors, including the mean across all priors (± standard deviation)...... 68

Acknowledgements

This project would not have been possible without the help of many people, and I would like to extend my sincere thanks to those that have helped throughout this long, challenging and intensely rewarding journey this year. Firstly, to my primary supervisor Dr Jennifer Chaplin, thank you for your willingness to take on a genetics-naïve Honours student and your patience in teaching me ‘the right way to do things.’ For your assistance with scholarship and postgraduate applications. For always making time to be on hand when I needed advice or guidance and for believing in me, thank you. To my secondary supervisor, Dr David Fairclough, for your good humor and good advice, and for your assistance with collecting the wild adult (and wild juvenile) samples for this project. My thanks also goes out to all the Department of Fisheries staff and volunteers who assisted in this sampling, and especially to Dr Elaine Lek, for your good company, and mad fish processing skills.

Thank you Greg Jenkins, for sitting down with me at the start of this project and sparking my interest in pursuing this line of research, and for your work (and that of the staff at Challenger

TAFE) to collect, culture and release the juvenile fish that were the focus of this thesis. To

Frances Brigg, for your continued work at the State Agricultural and Biotechnology Centre, for helping trouble-shoot issues, for not minding me being under-foot in your lab and for your fantastic ability to operate equipment I would be scared myself to even touch.

Thank you Michelle Gardner for your work optimizing the loci used in this study (thanks for doing all the hard work for me), for passing on your wisdom on all things snapper, and all things genetics, and your patience in teaching me. To Myrto Robert, for all your hard work in the lab, assisting in turning the data collected for this thesis into a complete dataset worthy of publication. To Brian Poh, for being a good-humored lab partner, for snowfall fights and nugget-runs at inappropriate times of night. Thank you for understanding the struggles of

xii genetics, and struggling with me. The prawns will get there soon, Brian. To Dr James

Tweedley, for your always entertaining stories and sage advice. To my partner in crime, Keyley

Hogan-West, for putting up with me for a whole year straight (I’m sorry for the therapy you now require), for providing much-needed comic relief and making experiences such as working at university until 3am, somehow, fun.

To my family and friends, from whom I have fielded numerous questions to the effect of: “How long until you finish?” “What are you studying again..?” and my personal favourite: “why don’t we ever see you anymore?” Thank you, for being understanding of my absence this year.

To my partner Riley, for understanding what this year means to me, for supporting me and giving me space when needed and encouraging me to achieve all the things I aimed to this year.

Lastly, I would like to extend my thanks to the funding bodies for this project. Without your support this study would not have been possible: to Recfishwest, conveyor of the Recreational

Fishing Initiatives Fund, for making this culturing trial possible. To Challenger TAFE, for recognising the importance of genetic implications in stock enhancement and for commissioning this research. And, to Richard and Michael Calver, for your support of the

Calver Family Scholarship, which has provided the financial support that allowed me the freedom to dedicate time to this thesis, to make it the best it could be.

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Chapter One – Introduction

1.1 Context of introduction

The first chapter of this thesis was prepared as a review of the literature relevant to this study on the genetic implications of culturing Chrysophrys auratus from wild-caught eggs for stock enhancement, in accordance with the instructions given. As such, it incorporates some secondary detail, which might not otherwise be included in a general thesis introduction, written from first principles. This chapter concludes with the significance, need for, and specific aims of the current study. The references have been compiled according to the style of the journal Aquaculture (see https://www.elsevier.com/journals/aquaculture/0044-8486/guide- for-authors#68000).

1.2 Active fisheries management

Many of the world’s fisheries continue to be fished at unsustainable rates. Since the mid-1980s, global harvests of wild-capture fisheries have been declining at a rate of around

0.7 t year-1, despite continued increases in global fishing effort (Pauly and Froese, 2012; Pauly et al., 2002). Currently 28.8% of capture fisheries are considered, by the Food and Agricultural

Organisation (2014), to be fished unsustainably, while Worm et al. (2009) argue that 63% of wild-capture fisheries, for which good data exists, have biomasses below the maximum sustainable yield (MSY). The demand for protein from fish is predicted to increase in future, placing even greater pressure on wild-capture fisheries and aquaculture to meet this growing demand (Bostock et al. 2010; FAO 2014).

In the face of such pressures, it has been widely accepted that active, adaptive management is required to rebuild over-exploited fish stocks, and maintain the harvest rates of

Chapter One – Introduction others at sustainable levels (Dayton et al., 2000; Leber, 2013; Lorenzen et al., 2013; Worm et al., 2009). Yet traditional means of restoring fish stocks via fishing restrictions and habitat and species protection within marine protected areas (Lorenzen et al., 2013; Pauly et al., 2002,

1998; Worm et al., 2009), are often economically, socially and/or politically unappealing

(Grimes, 1998; Worm et al., 2009). Furthermore, Støttrup & Sparrevohn (2007) argue that where multiple pressures are acting on a fishery, such as environmentally-induced recruitment bottlenecks, compounded by overfishing, simply reducing fishing mortality may not be enough to stimulate recovery. Consequently, fisheries managers are turning to alternate means of improving the sustainability of fisheries, such as stock enhancement (Lorenzen et al., 2013).

1.2.1 Stock enhancement for the management of fisheries

The production of fisheries in many coastal systems is limited by variability in recruitment success, i.e. the number of fish surviving past the vulnerable pelagic larval phase to enter a population, or fishery (Doherty, 1999; Munro and Bell, 1997). Therefore, stock enhancement aims to augment the natural supply of juveniles by releasing cultured juveniles into the wild, thereby optimizing harvests by overcoming this recruitment limitation (Bell et al.,

2008; Munro and Bell, 1997). This is distinct from restocking, which can be thought of as

‘conservation stocking.’ It aims to restore depleted spawning biomass or re-establish locally- extinct populations, by releasing cultured juveniles into the wild. Stock enhancement is also distinct from sea ranching, where cultured juveniles are released into enclosures within marine or estuarine environments in ‘put, grow, take’ aquaculture operations, i.e. fish are not released into the wild (Bell et al., 2008).

The commercial-scale release of cultured juveniles into the wild began ca. 150 years ago with releases of salmonids (Salmonidae), and marine finfish such as cod and sea bream in locations across the globe (Blaxter, 2000; Fushimi, 2001; Grimes, 1998; Molony et al., 2003).

Chapter One – Introduction

Belief in the potential of stock enhancement to yield significant social, harvest and ecological benefits (Lorenzen, 2005), has seen its application increase greatly since the early 1980s. The decision to enhance has however, often been driven by political factors, as stock enhancement is often more socially-acceptable than stringent fishing controls, or the technological capacity to produce large numbers of fish of a particular species (Bell et al., 2006; Camp et al., 2013;

Grimes, 1998; Mitchell et al., 2008; Molony et al., 2003). These motivations, combined with a lack of engagement from fisheries scientists (Lorenzen, 2014), saw an ad hoc approach applied to stock enhancements, with little planning or consideration given to the effectiveness of programs (Blankenship and Leber, 1995; Blaxter, 2000; Leber, 2013). Consequently, many early enhancement programs (especially pre-1980s) were criticized from economic (Grimes,

1998; Hilborn, 1998) and ecological standpoints (Bell et al., 2006; Blaxter, 2000; Lorenzen,

2005; Molony et al., 2003; Southward et al., 2005). With these early enhancements, the primary measure of effectiveness was the ability to increase the abundance of the receiving population

(Blaxter, 2000) and, there was initially little evidence that stock enhancement could actually achieve this goal (Bell et al., 2006; Blaxter, 2000; Lorenzen, 2005; Molony et al., 2003;

Southward et al., 2005).

Throughout the late 1980s and early 1990s, there was a push from fisheries scientists to better understand the ecological factors contributing to this lack of success (Blaxter, 2000). We now know that the survival of released fish is influenced by: density-dependent growth and mortality (Hühn et al., 2014; Lorenzen, 2008; Taylor et al., 2013), the appropriateness of release strategies, such as size and location of release (Hervas et al., 2010; Rowland, 2013), the extent to which there are surplus resources to support the releases (Kitada and Kishino, 2006;

Seitz et al., 2008) and the harvest dynamics of the fishery into which these fish enter (Rogers et al., 2010). As our understanding of the complex ecological interactions influencing enhancement success developed, guidelines for best practices began to emerge. In 1995, the ten

Chapter One – Introduction components of the ‘responsible approach’ to stock enhancement were established, which included guidelines on planning, culturing practices, release strategies, assessment and governance (Blankenship and Leber, 1995). Since this time, several authors have expanded on

(see Munro & Bell 1997; Taylor et al. 2005) or provided updates to (Lorenzen et al., 2010), the components of responsible stock enhancement.

There is now an increasing body of literature demonstrating the ability of enhancements to increase fishery yields, when carried out according to such ‘responsible guidelines.’

Examples of abundance enhancement include: black sea bream, Acanthopagrus schlegelii, in

Japan (Gonzalez et al., 2008); scallops in New Zealand, Pecten novaezelandiae (Drummond,

2004) and Japan, Patinopecten yessoensis (Masuda and Tsukamoto, 1998); black bream

Acanthopagrus butcheri, in Western Australia (Gardner et al., 2013); gilthead sea bream,

Sparus aurata, along the Iberian peninsula (Sánchez-Lamadrid, 2002); red sea bream, Pagrus major, in Japan (Kitada and Kishino, 2006); Japanese flounder, Paralichthys olivaceus, in

Japan (Masuda and Tsukamoto, 1998) and barfin flounder, Verasper moseri, in Japan (Wada et al., 2014). As such, stock enhancement is now evolving from an exploratory research area into an accepted fisheries management tool and gaining momentum in its application in both developed, and developing countries (Garaway et al., 2006; Leber, 2013; Lorenzen et al.,

2013).

1.3 Genetic effects of stock enhancement

Since the late 1980s there has been a growing apprehension that the release of cultured fishes into the wild may constitute a significant threat to the fitness of wild conspecifics

(Blaxter, 2000; Ryman, 1997; Wang et al., 2002). This sentiment has been spurred by findings from salmonid enhancements (Cross and King, 1983; Hindar et al., 1991) and the effects of escapees from aquaculture (Youngson et al., 2001). Evidence of deficits in: overall fitness

Chapter One – Introduction

(Araki et al., 2008), behaviours influencing post-release survival (Hutchison et al., 2012) and disease resistance (Bartley et al., 2006; Skaala et al., 1990) in cultured fish, compared to their wild counterparts, only served to fuel these concerns. Issues first raised in salmonids, later spread to other species (e.g. Taniguchi et al., 1983) and as our understanding of genetics and molecular methods of analysis developed, concern quickly grew for the potential genetic effects of stock enhancement, and the fitness implications this might have (Blaxter, 2000; Le Vay et al., 2007; Ryman, 1991).

1.3.1 Tools for assessing the genetic effects of enhancements

A range of genetic markers, including allozymes, mitochondrial DNA (mtDNA), microsatellites, amplified fragment length polymorphisms (AFLPs) and single nucleotide polymorphisms (SNPs) are employed in addressing questions in fisheries management (Moran,

2002; Saura and Faria, 2011). Each type of molecular marker has strengths and weaknesses, and the appropriateness of their use depends on the question being asked (Saura and Faria,

2011). Where these questions relate to the genetic implications of stock enhancement, to date microsatellites have been the main marker of choice (Chistiakov et al., 2006; Grandjean et al.,

2009; Saura and Faria, 2011; Selkoe and Toonen, 2006). While new techniques, which hold promise for this field of research, are emerging thanks to the commercialization of next- generation DNA sequencing, or massive parallel sequencing (Moran, 2002; Narum et al., 2008;

Seeb et al., 2011; Shendure and Ji, 2008), where time and budgetary constraints exist, as with

Honours projects, microsatellite markers are likely to be the preferred marker (see also Gardner et al., 2013).

1.3.1.1 Microsatellites

Microsatellites have many names, including short tandem repeats (STRs), simple sequence repeats (SSRs) and variable number tandem repeats (VNTRs). Here they will be

Chapter One – Introduction referred to simply as microsatellites. These are neutral markers which are very common in non- coding regions of DNA and consist of mono- and up to hexa-nucleotide tandem repeat sequences (Ellegren, 2004). They are appealing in fisheries genetics applications for a range of practical reasons: their small size, robustness to DNA degradation, ability to amplify by PCR and inexpensive, rapid detection make them ideal markers. They are also appealing for a range of molecular reasons: they are co-dominantly inherited, hypervariable (high allele polymorphism) and highly heterozygous (Chistiakov et al., 2006; Ellegren, 2004; Saura and

Faria, 2011; Selkoe and Toonen, 2006), which makes them ideal for assessing genetic variability (Taniguchi, 2003). Their appeal also extends to statistical analysis, as large multi- locus microsatellite studies are amongst the most statistically powerful (Selkoe and Toonen,

2006) and may even have greater power than emerging markers, like SNPs, to distinguish between closely related individuals (Narum et al., 2008).

The use of microsatellite markers to address questions regarding the genetic implications of stock enhancement is predicated on the assumption that these 'neutral' markers provide a reliable measure of genome-wide genetic variation (Kirk and Freeland, 2011; Moran,

2002). One potential issue is that these 'neutral' microsatellite markers may not reveal quantitative genetic changes occurring at adaptively-significantly loci, which are of interest because they are thought to influence the expression of phenotypes of adaptive significance, thus determining the potential of a population to adapt to changing environments (Frankham,

1995; Kirk and Freeland, 2011; Moran, 2002). It is generally accepted that neutral markers cannot reveal selection processes occurring at adaptive loci (Kirk and Freeland, 2011). Yet our greater understanding of the evolutionary mechanisms underlying variation at neutral loci makes these markers less prone to unforeseen biases and suitable as a means of measuring, albeit indirectly, population-wide genetic variation (Moran, 2002).

Chapter One – Introduction

There is also concern that uncertainties associated with the complex and poorly understood microsatellite mutation mechanisms (the rates of which can be as high as 10-2 to

10-6 per generation) may cloud conclusions drawn from such studies (Ellegren, 2004).

Nevertheless, Selkoe and Toonen (2006) argue that uncertain mutation mechanisms are unlikely to be an issue when comparing variation in present-day populations at the same temporal scale. Other potential issues associated with microsatellites include the presence of null alleles, which are alleles that fail to amplify consistently. Null alleles, if unaccounted for, can introduce a range of biases including inaccuracy in estimates of genetic diversity and population differentiation (Chapuis and Estoup, 2007). Homoplasy, which occurs where alleles have the same observed fragment size but differ in their evolutionary origin, also has the potential to bias population genetic studies. However, the hypervariability of microsatellite loci has been shown to compensate for the effect of homoplasy, such that the actual risk of bias is insignificant (Estoup et al., 2002). Indeed, Selkoe and Toonen (2006) suggest that the aforementioned issues can largely be avoided through proper locus selection and optimisation.

Furthermore, they argue that the strengths and versatility of microsatellites far outweigh their potential draw-backs, and they advocate for the continued use of microsatellites in fisheries science applications.

1.3.2 Potential genetic effects of traditional stock enhancement

Concerns for the genetic effects of enhancements can be assigned to one of two categories: those that have the potential to occur during culturing, and those that have the potential to occur in the combined cultured- wild population upon the release of cultured juveniles (Figure 1.1).

Chapter One – Introduction

Figure 1.1: Summary of potential genetic effects that can occur at each stage of the culturing and enhancement process.

Chapter One – Introduction

1.3.2.1 Effects during culturing

The stages during the culturing process with the greatest potential to induce detrimental genetic effects, are those involving adult breeding fish (broodstock) selection and mating (Le

Vay et al., 2007). As a consequence of the constraints on holding large numbers of breeding fish, it is necessary to select a finite number of broodstock from which to produce cultured progeny (Figure 1.1; Sato et al., 2014). This may result in a genetic bottleneck or ‘founder effect’ due to the limited founding gene pool, analogous to that of a small population, and the subsequent loss of rare alleles and reduction in genetic diversity in juveniles cultured for release (Figure 1.1; Table 1.2; Tave, 1999; Le Vay et al., 2007; Lind et al. 2012). In their review of studies over the past 50 years which have investigated the effects of stock enhancement Araki & Schmid (2010) found that in 66% of genetic studies comparing wild and cultured fish, reductions in allelic richness were reported (Table 1.1), and in most cases attributed to the use of a small broodstock. This potential reduction in genetic diversity, which has been reported to reach magnitudes as large as 25% in red sea bream (P. major), may have significant implications for population evolutionary potential (Fisher, 1958) and fitness, as a consequence of the loss of adaptively-significant alleles (Reed and Frankham, 2003).

However, in a study of Atlantic salmon (Salmo salar) in Spain, (Machado-Schiaffino et al., 2007) found no difference in the genetic composition between the broodstock used in culturing and the wild adult population from which they were selected. Yet they found a significant reduction in the allelic richness of the cultured juveniles. Machado-Schiaffino et al.,

(2007) suggested that it is the mating systems in captivity (Figure 1.1): the uneven sex ratios, uneven family sizes (number of offspring contributed by each mating pair) and low numbers of effective breeders (Nb), and not the broodstock selection, that results in reductions in the genetic variation of cultured stocks. This is supported by other recent parentage and relationship studies of stock enhancement species, e.g. S. aurata (Sánchez et al., 2012). Low effective numbers of

Chapter One – Introduction breeders and skewed parental contributions have been reported for many species (Table 1.2), including steelhead trout (Oncorhynchus mykiss), where the harmonic mean Nb over 12 years of culturing was 15 times lower than that of their wild counterparts (Christie et al., 2012). The effective number of breeders refers to the effective population size (Ne, the size of a hypothetical ideal population that would undergo the same amount of genetic change as the actual population) over a single reproductive cycle, in the parental generation. This parameter,

Nb, describes the genetic characteristics of the captive cohort, as the rate of loss of diversity due to genetic drift (random shifts in allele frequencies in finite populations due to chance) and inbreeding, is inversely proportional to the effective population size (Fisher, 1958; Waples et al., 2014; Wright, 1931).

When closely related individuals mate, for example when broodstocks are constructed from fish that have spent multiple generations in captivity, inbreeding depression can occur

(Wang et al., 2002). The result is an increase in homozygosity and increased probability of deleterious recessive alleles accumulating. This in turn can reduce the overall fitness (growth and survival) and adaptive potential (loss of heterozygous advantage) of the cultured fish

(Wang et al., 2002; Ward, 2006). Inbreeding depression in cultured fish is attributed to small broodstocks or uneven family contributions, where the number of effective breeders is small and broodstock are retained for successive generations (Wang et al., 2002; Ward, 2006).

Another concern during culturing is that fish raised in a hatchery environment tend to become adapted to their artificial surroundings, a phenomenon known as domestication selection (Figure 1.1; Hutchison et al., 2012). In the hatchery environment, relaxed selection pressures on traits favourable in the natural environment and increased selection pressures on those favourable in a hatchery environment can promote the positive selection or fixation of alleles which may be deleterious to survival upon release into the wild (Lynch and Hely, 2001).

Chapter One – Introduction

Domestication selection can act on both physical and behavioral traits affecting fitness.

Blanchet et al. (2008) found detrimental hatchery selection acting on fin length and body shape, traits which are important for swimming ability and survival upon release. Behavioural deficiencies such as failure to exhibit predator avoidance behavior in red drum, Sciaenops ocellatus (Stunz and Minello, 2001) and deficiencies in live-feeding behavior in pellet-fed turbot, Scophthalmus maximus (Ellis et al., 2002) have also been observed. Araki, Cooper and

Blouin (2007) found that the genetic effects of domestication on steelhead trout reduced reproductive fitness by 40% per captive-reared generation. However, such results appear to vary greatly between species and locations of release. Araki and Schmid (2010) noted in their review of genetic studies of enhancements that domestication effects were reported to result in a reduction in overall fitness in 57% of studies. Reduced survival was reported in only 19% of studies and neutral effects were found in 29% of studies assessed (Table 1.1; Araki and Schmid

2010).

Effect in hatchery fish compared to wild Fitness (21 studies) Diversity (32 studies)

Reduction in overall fitness 57%

Reduction in reproductive fitness 38%

Reduction in survival 19%

Neutral effects on fitness 29%

Reduction in diversity 66%

Reduction in heterozygosity 28%

Chapter One – Introduction

1.3.2.2 Effects in the wild

As discussed in the previous section, a reduction in the effective population size in the hatchery can quickly erode the genetic variation of cultured fish. Upon release, the detrimental effects of a small Ne in a cohort of cultured juveniles can be transferred to the combined wild- cultured population, in what is known as a Ryman-Laikre effect (Figure 1.1; Ryman and Laikre

1991). The Ryman-Laikre effect describes the reduction in the effective population size of a wild population, when individuals descended from a small number of founders (e.g. as a consequence of traditional culturing practices [Lind et al. 2012]) are added to a wild population

(Ryman and Laikre, 1991). This occurs according to the equation:

1 푥2 (1 − 푥)2 = + 푁푒 푁푐 푁푤

Where 푥 is the relative contribution of cultured progeny, of effective size Nc, and (1 −

푥) is the relative contribution of the progeny of wild fish, of effective size Nw. This effect has been observed for example, in S. ocellatus (Table 1.2), and also in O. mykiss, where a doubling of the number of cultured fish released into the wild, reduced the effective population size of the wild-cultured steelhead population by two thirds (Christie et al., 2012). The reduction in Ne of the wild population can lead to further adverse genetic effects such as erosion of diversity due to drift, which may reduce the fitness of the wild-cultured population (Rogers et al., 2010;

Ryman and Laikre, 1991; Ryman, 1997).

A related consequence of the reduction in Ne of the wild-cultured population can be an increase in the rate of inbreeding (Figure 1.1), leading to a loss of genetic diversity and potential fixation of deleterious alleles (Charlesworth, 2003; Hare et al., 2011). Indeed, in their review of the genetic risks to the adaptive potential of small populations, Willi et al., (2006) cautioned that the effects of inbreeding depression likely have a greater capacity to reduce evolutionary potential, than reductions in genetic variation alone. Rollinson et al. (2014), in

Chapter One – Introduction their study of salmonids which were bred in order to display inbreeding depression, found that inbred populations always exhibited decreased body size and lower survival rates compared to wild fish. However, Araki et al. (2008) suggest that the effect of inbreeding is unlikely to be the primary factor generating the magnitude of fitness declines observed in the wild. For example Araki et al., (2007) and Araki and Schmid (2010) found little evidence, in studies over the past five decades, of strong inbreeding in wild populations as a result of enhancement programs.

Another concern for enhancements is that, where spatial population structures are not identified prior to enhancement, or where broodstocks are collected from non-local sources for enhancement, introgression may result from releases (Figure 1.1; Ward, 2006). Introgression occurs where genetically distinct populations or sub-populations mate and consequently, repeated back-crossings occur between the alleles of the resultant hybrids. This has been observed for example in chum salmon (Oncorhynchus keta) in Alaska, where a significant shift in SNPs towards hatchery allele frequencies has been observed, following 30 years of introgression from hatchery escapees (Jasper et al., 2013). While early generations of progeny from populations exhibiting introgression can display heterosis (hybrid vigor), this advantage tends to diminish with successive generations (Ward, 2006). The instability in allele frequencies introduced by introgression may exacerbate the effects of genetic drift and deleterious alleles may accumulate in high frequencies over relatively few generations (Lynch and Hely, 2001; Ryman, 1997). Therefore, there is concern that introgression may reduce survival and reproductive fitness (Bekkevold et al., 2006).

Additionally, introgression of wild fish with genetically depauperate or maladapted cultured fish may lead to outbreeding depression (Bekkevold et al., 2006). Outbreeding results from mating between individuals from genetically distinct populations or sub-populations

Chapter One – Introduction which are adapted to their local environment (Rollinson et al., 2014). This can lead to outbreeding depression (Figure 1.1), or the loss of fitness due to the disruption of local adaptation/co-adapted gene complexes (Le Vay et al., 2007; Rollinson et al., 2014). Yet both positive and negative effects on growth and survival have been observed in S. salar (Rollinson et al., 2014). Weeks et al. (2011) also down-plays the potential effects of outbreeding depression, arguing for the capacity for heterosis to increase fitness and for natural selection in the wild to purge maladapted alleles.

1.3.3 The salmonid bias and marine enhancements

There is a significant bias in the literature on the genetic effects of enhancements, towards species from the family Salmonidae (Araki and Schmid, 2010; Utter, 1998). In their review of the literature from the past five decades regarding the effects of stock enhancement,

Araki and Schmid (2010) found that, of the genetic studies comparing wild and cultured genetic fitness (23 studies), 52% dealt with salmonid species. This focus on salmonids is a consequence of their long history of enhancement (Utter, 1998), the high-profile collapses of many wild salmonid fisheries (Utter, 2004) and the well-documented, and often negative, genetic effects associated with these enhancements (Araki and Schmid, 2010; Utter, 1998). The fine-scale genetic structure of salmonid populations, as a consequence of their anadromous life history, has long been an issue for enhancements (Hindar et al., 1991; Utter, 1998). Often broodstocks have consisted of a small number of fish, sourced from locations that differ from those of the intended receiving population, such that the release of their progeny have led to introgression and (arguably) outbreeding depression (Mobrand et al., 2005; Tringali and Bert, 1998; Ward,

2006; Youngson et al., 2001).

This focus on salmonids has contributed to the overwhelmingly negative view of the genetic implications of stock enhancement (Araki et al., 2008; Hindar et al., 1991; Utter, 1998).

Chapter One – Introduction

Yet in their review, Araki and Schmid (2010) found that, while there is undoubtedly evidence of negative genetic effects resulting from enhancements, these effects are dependent on factors such as the molecular markers used, the life history characteristics of the species of interest and the broodstock management practices (Tables 1.1; 1.2). These complexities are not always taken into consideration when viewing enhancements of marine finfish. The greater genetic continuity characteristic of marine species (Tringali and Bert, 1998; Ward, 2006), means there is a lesser risk of effects such as introgression, compared to salmonids (Utter, 1998). However, marine finfish typically have high fecundity and low effective numbers of breeders, due to their mating mechanisms (Murua and Saborido-Rey, 2003), and this makes them susceptible to reductions in Ne and losses of genetic diversity during culturing (Table 1.2; Utter, 1998).

A review of literature on the genetic effects of marine finfish stock enhancements reveals common trends of reduced allelic diversity and (to a lesser extent) heterozygosity in cultured fish, and genetic differentiation between wild and cultured fish (Table 1.2). It should be noted however, that many of these large-scale programs for marine finfish enhancement began in the 1980s (Table 1.2), before molecular markers were widely used and before the introduction of the ‘responsible approach’ to stock enhancement. Therefore, many of these programs likely did not utilize ‘best practices’ for at least part of their enhancement history.

Chapter One – Introduction

Table 1.2: Summary of enhancement history, broodstock management (including % parental contributions) and genetic effects of marine finfish enhancements, for which genetic effects have been compared between cultured and wild populations, and for which long-term ( > 15 years) commercial- scale enhancements have taken place.

Common Species name Enhancement history Genetic effects Broodstock management References name Barfin Verasper moseri Since 1987 in Japan,  No significant differences in genetic diversity Broodstock of ~50 fish, (Romo et al., 2005) flounder tens of thousands  Population differentiation between wild fish mostly wild, some released annually and fish cultured from captive broodstock cultured-wild mixes Low effective breeders (60%) Black sea Acanthopagrus Since 1982 in Japan,  Reduced genetic diversity in cultured fish Broodstock of 50-100 fish (Gonzalez et al., 2008; bream schlegeli ~1 million released  Neutral effect on heterozygosity Low effective breeders Jeong et al., 2007; annually  No differentiation between wild and cultured (~16-26%) Taniguchi et al., 1983) fish Gilthead Sparus aurata Since the mid-1980s in  Slight reduced genetic diversity in cultured fish Not reported (Alarcón et al., 2004; sea bream Southern Europe,  Significant differentiation between wild and Sánchez-Lamadrid, 2002) ~10,000 released cultured fish annually Japanese Paralichthys Since 1998 in Japan,  Significantly reduced genetic diversity in Broodstock 50-100 fish (Liu et al., 2006; Sekino et flounder olivaceus hundreds of thousands cultured fish Moderate effective al., 2003, 2002) to tens of millions  Loss of rare alleles breeders (~50%), instances released annually of single-male parentage contributions Japanese Scomberomorus Since 1998 in Japan,  Significantly reduced genetic diversity in Broodstock < 50 wild fish (Nakajima et al., 2014; Spanish niphonius 50,000-100,000 cultured fish Obata et al., 2008) mackerel released annually  Loss of rare alleles  Observed lower heterozygosity in cultured fish, but not significant  Differentiation between wild and cultured fish  Neutral effect on diversity of wild populations  No evidence of Ryman–Laikre effect Large Larimichthys Since mid-1980s in  Significantly reduced genetic diversity in Broodstock < 50 fish (Wang et al., 2012) yellow crocea China, ~1 million cultured fish Broodstock kept for two to croaker released annually  Reduced heterozygosity three generations  Significant differentiation between wild and cultured fish

Chapter One – Introduction

Table 1.2 continued: Summary of enhancement history, broodstock management (including % parental contributions) and genetic effects of marine finfish enhancements, for which genetic effects have been compared between cultured and wild populations, and for which long-term ( > 15 years) commercial-scale enhancements have taken place.

Common Species name Enhancement history Genetic effects Broodstock management References name Pacific Polydactylus Since 1993 in Hawaii,  No significant differences in genetic diversity Broodstock of ~11 wild (Leber et al., 1998; Wang Threadfin sexfilis tens of thousands  No differentiation between hatchery and wild fish et al., 2010) released annually Low effective breeders (46%) Red drum Sciaenops Since the early-1980s  Significantly reduced genetic diversity in Broodstock < 50 fish (Gold et al., 2008; ocellatus in the northern Gulf of cultured fish Low effective breeders Karlsson et al., 2008; Mexico, tens of  Potential for a Ryman–Laikre effect (~12-30%), instances of Liang, 2006) millions released single-male contributions annually to offspring Red sea Pagrus major Since 1963 in Japan,  Reduced genetic diversity in cultured fish Broodstock of 100–250 (Gonzalez et al., 2013; bream tens of millions  Loss of rare alleles fish Nugrohoa and Taniguchi, released annually  Neutral effect on heterozygosity Low effective breeders 2004; Perez-Enriquez et  Differentiation between wild and cultured fish (~17-40%), male-skewed al., 1999) sex ratios Turbot Scophthalmus Since the late-1980s in  Reduced genetic diversity in cultured fish Broodstock of different (Bouza et al., 2007, 2002; maximus Europe, 50,000-  Reduced heterozygosity geographic origin to Danancher and Garcia- 100,000 released  Some inbreeding observed in cultured fish receiving population Vazquez, 2011) annually White sea Diplodus sargus Since the late-1980s in  Significantly reduced genetic diversity in Wild broodstock held for (González-wangüemert et bream the Gulf of cultured fish two to four years, enriched al., 2012) Castellammare Italy,  Reduced heterozygosity with new individuals from ~2,000 released  Significant differentiation between wild and the wild annually cultured fish

Chapter One – Introduction

1.3.4 Managing genetic effects

The need for genetic resource management to avoid potentially adverse genetic effects from enhancements has long been recognised by the ‘responsible approach’ to enhancement:

“Use genetic resource management to avoid deleterious genetic effects”

(Blankenship & Leber 1995, component four)

“Use genetic resource management to maximize effectiveness of enhancement and

avoid deleterious effects on wild populations.” (Lorenzen, Leber & Blankenship 2010,

principle eight)

In order to achieve such goals, the aims of genetic resource management for stock enhancement should be to: maintain genetic diversity, such that the genetic composition of the cultured fish reflects that of the receiving wild population, maintain effective population sizes during culturing and minimize domestication selection (Le Vay et al., 2007; Ryman, 1991).

Many of the potentially negative genetic effects of enhancement can be addressed through management of genetic resources at key phases during the culturing process: i) collection of broodstock, ii) design of mating regimes and iii) rearing and release of juveniles into the wild

(Le Vay et al., 2007). Protocols for genetic management have been developed for each of these stages (see Mobrand et al., 2005; Le Vay et al., 2007; FAO, 2008; Rowland, 2013).

i) The genetic stock structure of the receiving population should be identified (Lorenzen et al., 2010; Saura and Faria, 2011; Ward, 2006). Broodstock should be collected from local populations and reflect the genetic composition of the receiving population, in order to minimize the risk of outbreeding depression (Rollinson et al., 2014). The numbers of broodstock should be sufficiently large to ensure a large effective population size and to thereby reduce the potential for loss of rare alleles and genetic diversity (Le Vay et al., 2007;

Tave, 1999; Tringali and Bert, 1998; Wang et al., 2002). In Arctic charr (Salvelinus alpinus)

18 Chapter One – Introduction the average allelic richness observed in juveniles cultured for release was positively correlated with the number of broodstock used to produce them. However, the exact number of broodstock required depends on the species mating mechanisms and hatchery practices, but should in any case be based on the Ne of the broodstock, rather than absolute numbers (see

Fraser, 2008). Recommendations for effective population sizes to avoid inbreeding depression are > 50, and to avoid a loss of evolutionary potential, > 500 (Franklin and Frankham, 1998;

Palstra and Ruzzante, 2008).

ii) Molecular methods for the selection of breeding individuals should be employed, where possible, to minimize co-ancestry and inbreeding and increase genetic variation (Borrell et al., 2007; Lind et al., 2012; Taniguchi, 2003). The mating design should equalize sex ratios and equalize family sizes (each individual contributes the same number of offspring to the next generation, as this doubles the Ne, relative to random mating) in order to maximise genetic diversity and the number of effective breeders (Fraser, 2008; Le Vay et al., 2007). Fiumera et al. (2004) recommend a factorial mating design to achieve this, where eggs from each female broodfish are artificially fertilised with sperm from each male.

iii) While it appears that domestication selection due to exposure to a captive environment may be unavoidable during culturing (Blanchet et al., 2008; Fraser, 2008; Utter,

2004), a period of acclimatisation to conditions in the wild (Hervas et al., 2010) or techniques to mimic natural environments in the hatchery may go some way towards avoiding this

(Hutchison et al., 2012; Le Vay et al., 2007). Additionally, reducing the duration held in captivity to a single generation, may be beneficial (Fraser, 2008; Le Vay et al., 2007; Milot et al., 2013).

Chapter One – Introduction

1.3.5 Novel techniques

The considerable variation in reproductive success and low numbers of effective breeders observed in many commercially enhanced species (Table 1.2) necessitates the upkeep of very large numbers of broodfish (Fraser, 2008; Gruenthal and Drawbridge, 2012). However, this requires significant monetary investment, as well as sufficient space and skilled labour

(Sato et al., 2014). In an attempt to circumvent the challenges associated with holding large broodstocks, and in order to conserve the genetic diversity and fitness of populations, a number of novel culturing techniques have been trialed or proposed (Fraser, 2008). Proposed techniques have included spermatogonial transplants. For example, Sato et al. (2014) trialed a method of transplanting sperm from several donor male fish into a single female recipient.

They reported significant increases in the genetic diversity of progeny with this technique, compared to traditional methods. Novel egg harvest strategies have also been proposed. For example, one method involved the collection of eggs of the broadcast spawning A. schlegelii at two-hourly intervals, which resulted in a two- to three-fold greater allelic richness compared to traditional single-batch egg collection techniques (Gonzalez et al., 2010).

Another proposed egg harvest strategy is the collection of fertilised eggs and larvae from the wild, their culturing in a hatchery to overcome recruitment bottlenecks and then the release of these fish into the wild as juveniles (Munro and Bell, 1997). Such a method would maintain natural mating regimes, which Quinn (2005) argues is essential to facilitate sexual selection which maintains fitness in the wild, and which Neff et al. (2011) suggest, may assist in maintaining genetic diversity. A preliminary study by Crossman et al. (2011) compared the diversity and relatedness of lake sturgeon (Acipenser fulvescens) under different fertilization methods for restocking. They found that dispersal-stage larvae and naturally-fertilized wild eggs collected for culturing had a lower mean relatedness and co-ancestry and a greater number of effective parental breeders compared to traditional, artificial fertilization techniques

Chapter One – Introduction

(Crossman et al., 2011). Therefore, there appears to be both theoretical (Munro and Bell, 1997;

Ward, 2006), and practical (Crossman et al., 2011) evidence to support the claim that wild egg collection techniques may have positive genetic outcomes in a stock enhancement context.

1.4 This study

1.4.1 Project overview

Egg and larvae wild-capture culturing methods have been employed for the culture of finfish, including carp (various species), mullet (Mugil spp.) and milkfish (Chanos chanos) for aquaculture purposes (Pillay and Kutty, 2005), ornamental tropical fishes for the aquarium trade (Bell et al., 2009), and Acipenser fulvescens for restocking (Crossman et al., 2011), which was the only program to assess genetic implications. Yet this technique had not been applied to a marine finfish for the purpose of stock enhancement until, in 2014, the Australian Centre for

Applied Aquaculture and Research (ACAAR) at Challenger TAFE in Fremantle, Western

Australia trialed a novel technique for the culture of the Australasian snapper, Chrysophrys auratus. This technique involved the collection of fertilised eggs from spawning aggregations of C. auratus in Cockburn Sound and the culture of these eggs to juvenile stage for release

(Figure 1.2; see Chapter Two), hereafter referred to as ‘the ACAAR technique.’

This thesis describes a study of the genetic implications of the ACAAR technique for culturing Chrysophrys auratus from wild-caught eggs for the purpose of stock enhancement

(Figure 1.2). As the previous sections demonstrate, there is a large degree of variation in responses to culturing and stock enhancement between species, locations and culturing techniques (Table 1.2). Therefore, a sound understanding of the biology of C. auratus is necessary to inform an assessment of the genetic implications of this particular technique.

Chapter One – Introduction

1.4.2 Study species general biology

1.4.2.1 Taxonomy

More than 20 scientific names have been used to describe Chyrsophrys auratus throughout its range, and four have been attributed to this species in Western Australia alone (Table 1.3; Paulin, 1990). The biology of C. auratus varies greatly between regions, and therefore a clear understanding of the taxonomy of this species is necessary to identify its biological characteristics in the region of interest. The taxonomic classification of C. auratus was historically based on morphological characteristics such as dentition, and jaw and head morphology (Paulin, 1990). Studies employing such techniques repeatedly classified, and re-classified C. auratus at both the species and genus level. Much of the taxonomic contention centered on whether the northern and southern hemisphere populations were indeed separate species (Table 1.3).

Table 1.3: Summary of scientific names that have been attributed to Chrysophrys auratus and have received attention in scientific debate over the past 50 years regarding its taxonomy.

Northern Southern hemisphere hemisphere Japan and Australia New Zealand China Western Eastern Australia Australia Chrysophrys C. auratus X X X (Quoy & (Bloch & Schneider 1801) Gaimard 1824) C. unicolor X X (Quoy & Gaimard 1824) C. major X (Temminck & Schlegel 1843) Pagrus P. auratus X X X X (Cuvier 1816) (Bloch & Schneider 1801) P. unicolor X (Quoy & Gaimard 1824) P. guttulatus X (Valenciennes 1830) P. major X (Temminck & Schlegel 1843)

23 Chapter One – Introduction

Mitochondrial DNA analysis has revealed that differentiation between the populations in each hemisphere occurs at the subspecies level (Tabata and Taniguchi,

2000). Additionally, as a result of molecular research revealing Pagrus to be a non- monophyletic grouping (Chiba et al., 2009; Orrell and Carpenter, 2004), the genus

Chrysophrys has been readopted in Australia and New Zealand. The southern hemisphere subspecies has subsequently been reclassified as Chrysophrys auratus (Bray and Gomon,

2012; Gomon et al., 2008; Parsons et al., 2014). In Western Australia, C. auratus is commonly known as pink snapper, but the species will be referred to in this thesis as

‘Australasian snapper,’ or in reference to the specific population of interest: ‘Cockburn

Sound snapper.’

1.4.2.2 Distribution

Australasian snapper are widely distributed between ca. 18oS and ca. 40oS, throughout tropical and temperate coastal waters of southern Australia and New Zealand. In

Western Australia, C. auratus occurs from the South Australian border in the south, to

Barrow Island in the north (Figure 1.3; Kailola et al., 1993; Bray and Gomon, 2012). Three major population centers have been identified in Western Australia: the Gasgoyne in the north (Carnarvon, Shark Bay, Kalbarri, ca. 25oS), and the Perth metropolitan (Cockburn

Sound, Owen Anchorage, Warnbro Sound, ca. 32oS) and Southwest (Augusta, ca. 34oS) populations in the south (Wakefield, 2006). The following discussion of the biology of C. auratus will focus on the Cockburn Sound population (32.205° S, 115.724° E).

1.4.2.3 Reproduction and recruitment

Cockburn Sound snapper reach maturity at 585 mm for females and 566 mm for males at ca. 5.6 years. Upon reaching maturity, C. auratus begin to take part in annual inshore-offshore migrations, travelling inshore to form spawning aggregations (Wakefield,

2006), which are gatherings of conspecifics for the purpose of spawning (Domeier and

Colin, 1997), within shallow marine embayments. Cockburn Sound and adjacent Owen

Chapter One – Introduction

Anchorage and Warnbro Sound are important spawning sites for C. auratus (Wakefield,

2006; Wakefield et al., 2011), and Cockburn Sound is considered to be the most significant spawning aggregation site on the lower west coast of WA (DoFWA, 2010).

Spawning in Cockburn Sound occurs between September and January each year, when water temperatures are between 15.8 and 23.1°C (Wakefield, 2010). The period of peak spawning occurs between November and December, when water temperatures are between 19 and 20°C (Wakefield, 2010). These high water temperatures have been found to increase C. auratus larval survival (Fielder et al., 2005; Fowler et al., 2013; Murphy et al.,

2012). Additionally, during peak spawning in Cockburn Sound, prevailing southwesterly winds produce an anti-cyclonic eddy which promotes retention of C. auratus larvae, and also their planktonic prey, within the embayment (Doak, 2004; Holliday et al., 2011;

Wakefield, 2010).

Chrysophrys auratus are highly fecund broadcast, multiple batch spawners (see

Wakefield 2006). Consequently, they demonstrate the low egg and larval survival (Zeldis et al., 2005), and high variance in reproductive success (Hamer and Jenkins, 2004), characteristic of Type III survivorship curves that are common to highly fecund marine fishes (Hedrick, 2005). As a consequence of this reproductive strategy, the abundance of 0+

(cohort of fish < 1 year) C. auratus varies greatly from year to year, by as much as 20-fold in some regions (Fowler et al., 2013). In Cockburn Sound, C. auratus exhibits inter-annual variation in recruitment success and as a result, population age structures are dominated by a few strong year classes (Wakefield, 2006). This indicates that, as with other populations of C. auratus (Fowler and Mcglennon, 2011), recruitment success is likely to limit abundances of adult Cockburn Sound snapper.

Fish which survive the vulnerable early life stages, associate with the flat, sandy substrates of the 20 m deep central basin and dredged shipping channels of Cockburn

Sound, for the first two years of life (Wakefield, 2006; Wakefield et al., 2013). Cockburn

Chapter One – Introduction

Sound juveniles then move both north and southwards into nearby coastal waters at two to six years of age (Wakefield, 2006), where they associate with offshore reefs, in waters

< 200 m (Bray and Gomon, 2012; Wakefield et al., 2011). Tagging studies have shown that the majority of Cockburn Sound snapper disperse only small distances, less than 40 km.

However, there are also cases where individuals have travelled up to 134 km (Wakefield et al., 2011).

The stock structure of C. auratus on the lower west coast of Western Australia is not well understood (Fairclough et al., 2013). Tagging studies and otolith chemistry analysis have shown that Cockburn Sound and the adjacent marine embayments are important sources of recruits for the Perth Metropolitan management area (Figure 1.3), which exhibits substantial self-recruitment (Fairclough et al., 2013; Wakefield et al., 2011). There is also evidence that the Mid-west and South-west management areas rely on recruitment from other areas (Fairclough et al., 2013), and as Cockburn Sound is a significant spawning site, it is likely to contribute recruits to these adjacent areas (DoFWA, 2010). Yet the extent to which Cockburn Sound snapper mix with populations further afield e.g. from the northern

Gasgoyne (Shark Bay and Carnarvon) population, remains unclear (Fairclough et al., 2013;

Wakefield et al., 2011).

Chapter One – Introduction

1.4.3 Study species threats and management

Australasian snapper are highly sought-after by recreational and commercial fishers

(Henry and Lyle, 2003; Kailola et al., 1993; Willis et al., 2003). Yet the biology of C. auratus, in particular the formation of predictable spawning aggregations, makes them especially vulnerable to fishing pressure (Sadovy and Domeier, 2005). Chrysophrys auratus are long-lived and exhibit inter-annual variation in recruitment success (see section 1.4.2.3) and as a consequence, constant fishing effort may significantly reduce the biomass of poor recruitment year classes (DoFWA, 2010). Additionally, because C. auratus occupy deep waters, even catch-and-release fishing carries a risk of barotrauma-induced organ damage or mortality (Peregrin et al., 2015).

Chapter One – Introduction

Fisheries in Western Australia are managed at large bioregional scales and smaller within-bioregion zones which correspond to environmental and/or fishery characteristic boundaries (Figure 1.3; Fairclough et al. 2014). Cockburn Sound lies within the West Coast bioregion (WCB) and the fishery for C. auratus is managed under the West Coast Demersal

Scalefish (WCDS) fishery rules and regulations (Figure 1.3; Fairclough et al., 2014).

Historically, the WCDS fishery was managed as an open-access wet-line fishery (Jackson,

2007; Wakefield, 2006). But concerns for the unsustainable rate of harvests prompted the introduction of a management goal to reduce annual catches to fifty per cent of 2005/06 levels, a reduction to 303 t combined catch for commercial and recreational fisheries

(DoFWA 2010). In an effort to reach this management goal, various input and output controls for the WCDS fishery were implemented between 2008 and 2010. These included reduced total commercial catch (TCC) quotas, regulated by time-based units of entitlement and a reduction in the number of licences for commercial fishers. Additionally, for recreational fishers, bag limits were reduced from three to two, size limits south of 31°S

(Lancelin) were increased from 450 mm to 500 mm, the use of release weights (to reduce barotrauma) was made compulsory, and a state-wide recreational fishing from boat license was introduced (DoFWA 2010; Smallwood, Hesp & Beckley 2013).

In Cockburn Sound, commercial fishing for C. auratus has been limited.

Commercial catches in the embayment have generally been dominated by species like king

George whiting, garfish, Australian herring, various crab species and octopus (D.A. Lord &

Associates Pty Ltd, 2001; CSMC, 2002). Currently, only limited harvests of C. auratus

(0.7 t in 2009/10) are taken by the Cockburn Sound Line and Pot Managed Fishery

(DoFWA, 2010; Fairclough et al., 2014). Conversely, there is potential for large harvests of

C. auratus in Cockburn Sound by recreational fishers: Chrysophrys auratus is a very popular recreational species, the occurrence of high concentrations in Cockburn Sound during spring is well-known and the area is highly accessible to recreational fishers (CSMC,

2002; Sumner and Lai, 2012). However, most fishing for C. auratus in Cockburn Sound

Chapter One – Introduction takes place in the late evening and night-time, which has confounded attempts to gain an accurate estimate of recreational harvests for this species (Sumner and Lai, 2012; Sumner and Williamson, 1999).

This potential for high recreational harvests of C. auratus and the recognised importance of Cockburn Sound as a spawning aggregation site (Wakefield et al., 2011), motivated the introduction of time-area closures in 2000 (D.A. Lord & Associates Pty Ltd,

2001). A greater understanding of the biology of Cockburn Sound snapper, in combination with the 50% catch reduction target for WCDS, saw the initial annual September 15 to

October 31 closure extended to between October 1 and January 31 (DoFWA 2010). Yet

Støttrup & Sparrevohn (2007) argue that without complementary management tools, reducing fishing mortality may not be sufficient to rebuild fish stocks. Stock enhancement, when used in combination with fisheries management controls, such as those employed in managing Cockburn Sound snapper, can be effective in increasing abundances and fishery yields (Lorenzen, 2014) and might assist in achieving the sustainability goals of the WCDS fishery.

1.4.4 Motivation for enhancement

As a consequence of the biological characteristics of C. auratus in Cockburn Sound, this population represents both a unique opportunity to trial a novel, never before trialed, technique for culturing an aggregate spawning marine finfish for stock enhancement, and could also represent a good candidate for a responsible stock enhancement program.

1.4.4.1 Practicality

1.4.4.1.1 Egg collection

The trial of the ACAAR culturing technique would have been impractical were it not for the presence of Cockburn Sound snapper spawning aggregations adjacent to the

Perth metropolitan area. This characteristic offered a rare opportunity to undertake six weeks of sampling to collect a sufficient number of fertilised eggs for this trial, without

Chapter One – Introduction incurring prohibitively high operating costs (ACAAR 2014). The presence of such spawning aggregations, close to highly-developed metropolitan centers, is an uncommon occurrence and likely contributed to why the technique trialed by the ACAAR of collecting fertilised eggs of a marine finfish and culturing these for stock enhancement has (to the author’s knowledge) not previously been attempted and published.

1.4.4.1.2 Culture technologies

The desirability of Chrysophrys auratus as a species for aquaculture has long been recognised in Australia due to its reputation as an iconic recreational sport fish and a palatable table fish (Partridge and Jenkins, 2003; Quartararo, 1996). The ACAAR were at the forefront of early efforts to develop techniques for culturing C. auratus in Australia and, having overcome bottlenecks associated with larval weaning and rearing, published a hatchery manual on their culture (Battaglene and Talbot, 1992; Partridge and Jenkins,

2003). The ACAAR therefore had the skills and expertise to undertake the culturing of wild-caught C. auratus eggs without suffering excessive losses due to mortality, thus making the venture viable.

1.4.4.1.3 Funding

The funding and impetus for this stock enhancement trial came from the changing landscape of stock enhancement in Australia: a number of states have introduced recreational fishing licenses for marine and estuarine fisheries, which have become an important source of funding for the research and development of marine enhancement programs (Loneragan et al., 2013). In March 2010, the Department of Fisheries, Western

Australia (DoFWA) introduced a state-wide recreational fishing from a boat licence

(DoFWA 2010). Of the revenue generated by this licence, 25% has been directed into a

Recreational Fishing Initiatives Fund (RFIF), to be spent on projects aiming to benefit recreational fishers around the state (ACAAR 2014). RFIF funds have already been utilized for the restocking/enhancement of barramundi (Lates calcarifer), mulloway

Chapter One – Introduction

(Argyrosomus japonicas) and western school prawns (Metapenaeus dalli) in Western

Australia (Recfishwest, 2015). This financial source, combined with the iconic nature of the

Cockburn Sound spawning aggregations, provided the social and political driver that motivated this trial.

1.4.4.2 Responsible enhancement

1.4.4.2.1 Recruitment limitation

The responsible practice of stock enhancement dictates that programs should be initiated only where it can be demonstrated that recruitment is limited (Loneragan et al.,

2013; Støttrup and Sparrevohn, 2007). That is, where the natural supply of juveniles fails to utilize all the resources in the system, i.e. fails to reach the carrying capacity of the system

(Bell et al., 2006; Munro and Bell, 1997). Where this is the case, Southward et al. (2005) argues a species or population may be a good candidate for stock enhancement. Cockburn

Sound snapper exhibit variable inter-annual recruitment success, and the presence of infrequent strong year classes indicates that the carrying capacity of the system is seldom reached (Wakefield, 2006). While fine-scale annual catch data for the Cockburn Sound fishery are not available, evidence from other fisheries for C. auratus indicates that this recruitment variability may be responsible for the inter-annual variation observed in catches

(Fowler and Mcglennon, 2011; Fowler et al., 2013). Therefore, because recruitment is likely to be limited for Cockburn Sound snapper, the addition of cultured juveniles, as part of a carefully-planned enhancement program, has the potential to augment the natural supply of juveniles and increase adult abundances (Bell et al., 2008). However, long-term monitoring of such an enhancement program would be necessary to assess this potential

(Lorenzen et al., 2010) and such data are, as yet, unavailable for this trial.

1.4.4.2.2 Genetic implications

The broadcast spawning strategy employed by C. auartus, which includes high fecundity and high variance in reproductive success (see section 1.4.2.3), would likely

Chapter One – Introduction result in few effective breeders contributing to progeny, relative to the census size of spawning adults, in a culturing situation (see Gruenthal & Drawbridge 2012). Aquaculture operations for C. auratus have reported failure to ovulate and failure to spawn in a large proportion of captive females, further suggesting that numbers of effective breeders in captive C. auratus would likely be low (Quartararo, 1996). This could lead to a loss of genetic variation and increase in the rate of inbreeding amongst cultured progeny (see section 1.3.2.1). This phenomenon has been observed in related broadcast spawning sparids such as P. major and A. schlegelii cultured for enhancements (Table 1.2; Jeong et al., 2007;

Kitada et al., 2009; Gonzalez et al., 2010).

As discussed in section 1.3.2, efforts to maximise the number of effective breeders and retain genetic variation are imperative when culturing a species for stock enhancement.

The ACAAR technique ensures that the limit to the genetic diversity and effective number of breeders is defined, not by a finite number of broodstock and the mating design in the hatchery (Figure 1.1), but by the sampling regime for collecting wild eggs and the success of the sampled spawning event (Figure 1.2). The ACAAR ensured that the sampling of eggs took place during peak spawning periods, when the fraction of spawning females is greatest in Cockburn Sound (see Chapter Two; Wakefield, 2010). Therefore, this technique is likely to maximise the probability of capturing eggs from a large number of parental breeders and retaining the genetic diversity of the wild population.

1.4.5 Significance and need for this study

As discussed in the previous sections, the use of wild-caught eggs in the ACAAR technique, rather than broodstock, has significant cost advantages in the production of cultured fish for stock enhancement. This technique also has the potential to facilitate the capture of a representative sample of the genetic diversity present in the wild population and its transfer to cultured juveniles. This potential has, however, yet to be assessed.

Following the tenets of responsible stock enhancement, the genetic implications of the

Chapter One – Introduction

ACAAR technique must be thoroughly evaluated before it is put into practice (Lorenzen et al., 2010; Saura and Faria, 2011; Ward, 2006). The current study represents the first investigation into the genetic implications of a wild egg-collection method for culturing an aggregate spawning marine finfish. The findings of this study will inform about the efficacy of using such a technique for aggregate spawning marine finfish in general, and for

Cockburn Sound snapper in particular.

1.4.5 Study objectives

The overall objective of this study was to investigate the genetic implications, for wild stocks, of producing juveniles of C. auratus in a hatchery environment from fertilised eggs collected from spawning aggregations in Cockburn Sound (the ACAAR technique) and releasing them into the wild. As is common in studies of the genetic implications of culturing for stock enhancement, a before-after approach was employed. The genetic composition of a sample of wild adults of C. auratus collected from spawning aggregations in Cockburn Sound (before) was compared with a sample of juveniles of C. auratus cultured from wild-caught eggs collected from Cockburn Sound spawning aggregations

(after). The genetic compositions of the two samples, based on the multi-locus genotypes of each fish at nine microsatellite loci, were compared, with a view to establishing the extent of any differences between the two samples. In particular, the cultured juvenile sample was compared to the wild adult sample to identify evidence of: i) a reduction in genetic variation, ii) a bottleneck during sampling/culturing, iii) inbreeding, and iv) low numbers of effective breeders, as is often the case with traditional methods of culturing for stock enhancement. The results of these analyses were used to assess the potential for large-scale releases of ACAAR-cultured juveniles to alter the genetic composition and, in particular, reduce genetic diversity in the wild population.

Chapter Two – Methods

2.1 Sampling design and collection

2.1.1 Sample site

Cockburn Sound is a large, shallow, partially-enclosed marine embayment on the lower west coast of Western Australia (Figure 2.1). It is bound by Garden Island to the west and three shallow banks to the north, south and east (Sampey et al., 2011). They shelter the embayment from the moderate south-southwesterly swells, reducing the swell height to around 5% of oceanic heights (Simpson, 1996) and resulting in weak wind-driven and tidal circulation within the embayment (Steedman and Craig, 1983). Cockburn Sound supports important seagrass habitats (Kendrick et al., 2002) and considerable marine invertebrate and vertebrate assemblages, including 73 species of fish (Sampey et al., 2011), many of which are of fisheries or aquaculture importance (CSMC, 2002).

Cockburn Sound is the most intensively used marine embayment in Western

Australia, and is located adjacent to the highly developed Kwinana Industrial Area (CSMC,

2002). Anthropogenic modification of the embayment has resulted in significant environmental degradation, including increased levels of nutrients, phytoplankton biomass and chlorophyll a concentrations and subsequent reductions in available light and dissolved oxygen (D.A. Lord & Associates Pty Ltd, 2001; CSMC, 2002; Goodale and Smedley,

2013). The DoFWA (2010) has suggested that these conditions may affect the viability of

C. auratus spawning aggregations via reductions in: spawning biomass, egg and larval quality and growth of juveniles. Despite improvements for some environmental indices, and in some areas, the environmental condition of the embayment remains a source of concern

(Goodale and Smedley, 2013; Rose et al., 2012). Chapter Two – Methods

Figure 2.1: Location of the sampling site Cockburn Sound, a shallow marine embayment, in Western Australia. Depth contours of 0–10 m ( ) and > 10 m ( ) are shown for coastal waters. Adapted from: Wakefield (2010).

2.1.2 Sampling design

This study is based on genetic analysis of: i) wild adults of Chrysophrys auratus from 2014 Cockburn Sound spawning aggregations, and ii) juveniles of C. auratus cultured using the ACAAR technique, in order to compare the genetic compositions between these two samples. In addition, a sample of wild adults of C. auratus from 2013 Cockburn Sound spawning aggregations was collected, to assist in the development and refinement of the molecular methods used in this study.

Chapter Two – Methods

2.1.2.1 Wild adults (2014)

Wild adults of C. auratus were sampled between October 3 and December 5, 2014 from the following locations within Cockburn Sound: the D9 barge wreck site (32.1944°S

115.7442°E), Garden Island (32.1475°S 115.64245°E) and Sulfur Rocks (32.3108°S

115.7442°E). These locations were selected as they are known spawning aggregation sites and therefore, the sampled fish were likely to be representative of wild adults present in the

2014 spawning aggregations. These locations also overlapped with those sampled for wild- caught eggs by the ACAAR (see below). The fish were caught by bait and line methods by

DoFWA staff and volunteers, fishing from the 30 ft FD60, 28 ft RV George Cassells and, in the case of volunteers, private vessels.

From each adult fish, a small (approximately 2 cm x 0.5 cm) section of dorsal fin, which incorporated part of two or three adjacent fin rays and the interstitial tissue in between (together, the fin clip), was cut from the posterior margin of the fin using a sharp knife. The fin clips were placed in pre-prepared sample vials containing 100% ethanol. Data on the date of capture, location, fork length, total length, sex and whether external tags were present or inserted, were recorded. Fin clip samples from 94 individuals were sent to the author at Murdoch University. Fifty eight of these were selected for genetic analysis (based on whether there was sufficient easily-macerated interstitial tissue contained in the sample).

They consisted of 29 females and 29 males, ranging in fork length (FL) from 490- 845 mm, with a mean length of 746 (±11) mm. These 2014 fish will hereafter be referred to as ‘wild adults.’

2.1.2.2 Cultured juveniles

The juveniles in this study were cultured from fertilised C. auratus eggs collected from the water column above spawning aggregations in Cockburn Sound, during the 2014 spawning season. Sampling for wild eggs was initiated once water temperatures in

Cockburn Sound reached the range for peak C. auartus spawning (19- 20°C). Sampling

Chapter Two – Methods took place on nights when either a new or full moon was present, as female spawning fractions are greatest at these times (96-100% and 75% respectively [Wakefield 2010]), giving the highest probability of sampling eggs from a large number of C. auratus.

Sampling was also timed to coincide with peak egg densities which occur approximately three hours after the high tide (Wakefield, 2010). Wild eggs were collected by ACAAR staff on eight occasions over a six week period from October 8 to November 19, 2014.

However, only two of these sampling occasions, November 10 and November 17, resulted in sufficient numbers of C. auratus eggs (200,000 and 1,260,000 respectively) for culturing.

Sampling took place on Challenger’s 66 ft training vessel, the Maritime Image and the 26 ft Birkin. Spawning aggregations were identified using side sonar at known spawning aggregation sites within Cockburn Sound (see section 2.1.2.1). Following established methods for the collection of planktonic Cockburn Sound snapper eggs and larvae, double bongo nets of 60 mm diameter and 500 µm mesh were towed obliquely for 5 minutes at 1-2 knots (see Wakefield, 2006, 2010; Breheny et al., 2012) and approximately 30-40 m downwind of the identified spawning aggregation (ACAAR, 2015). A warp (length of rope) of approximately 250 m was used to sample the negatively buoyant eggs, which were found in highest concentrations in the top 50 cm of the water column (ACAAR, 2015). Upon retrieval, the nets were washed with seawater to dislodge plankton from the sides and deposit them in the cod end. The concentrated plankton samples in the cod end were then washed through a 1,000 μm sieve to remove large plankton, like jellyfish (Harris et al.,

2000). Samples were then distributed between multiple plastic bags to reduce plankton densities in each individual bag. The seawater was heavily oxygenated, and the bags inflated with pure oxygen and sealed for transport back to the ACAAR, where plankton samples were immediately placed in a 300 L flow-through tank.

Chapter Two – Methods

Following night-time collection of wild eggs, the following day the eggs were screened in order to retain those falling within the average C. auratus size range

(850-1,050 µm [Wakefield 2010]). The precision of this screening method for identifying

C. auratus eggs was assessed visually by ACAAR staff and was also validated by DoFWA staff, using a species-specific probe and real-time PCR methods (Dias et al. 2015), which confirmed the assigned visual IDs (ACAAR, 2015). The retained eggs were then disinfected with a hydrogen peroxide treatment (at a final concentration of 8 ppm H2O2 in seawater), and assessed for the viability of eggs following these treatments. Egg drop-out was high during these pre-hatching stages, at around 50%, although in the wild the pre-hatching mortality rate is greater, estimated as 83% over the same period (Zeldis et al., 2005).

Screened and treated eggs were transferred to an oxygenated 300 L flow-through incubation tank to hatch. Hatching took place approximately 36 hours post-collection, depending on the timing of sampling in relation to peak spawning and fertilisation.

The November 10 sampling trip yielded 200,000 eggs and from these, 29,000 snapper larvae hatched and were stocked into culture tanks. The November 17 sampling trip yielded 1,260,000 eggs, and from these, 21,600 snapper larvae hatched. Larvae were cultured in accordance with ACAAR semi-intensive greenwater culture methods (see

Jenkins and Frankish, 2003; ACAAR, 2015), until they were post-metamorphose at 40 days post-hatching and 20 mm mean total length for the November 10 sample, and 33 days post- hatching and 12 mm mean length for the November 17 sample. Survival between hatching and reaching these respective ages was 5.9% and 3.2% for the respective samples. The juveniles were fed on live feeds (i.e. not weened onto artificial feeds) for their entire duration in captivity, to maximize the probability of effective feeding upon release into the wild.

Chapter Two – Methods

Prior to release, and the collection of samples for genetic analysis, the otoliths of all cultured juveniles were marked by immersion in 10 ppm Alizarin Complexone (ALC) solution for 2 hours. This was done so that the cultured juveniles could be identified for analysis in years to come. On December 20, 2014 approximately 120 cultured juveniles were collected for genetic analysis. These individuals were euthanased in an ice slurry before being distributed between two jars containing 100% ethanol. These fish will hereafter be referred to as ‘cultured juveniles.’ On the same day, the remaining 2,500 juveniles cultured in this manner were released into Cockburn Sound.

2.1.2.3 Wild adults (2013)

Wild adults of C. auratus were sampled in September 2013 from Cockburn Sound by recreational fishers undertaking voluntary sampling for the DoFWA. Individuals of

C. auratus were caught by bait and line methods. A small (approximately 2 cm x 0.5 cm) section of dorsal muscle tissue was collected from each fish and placed in pre-prepared sample vials containing 100% ethanol. Samples from seven fish were sent to the author at

Murdoch University for genetic analysis.

Muscle tissue is ideal for use in molecular analyses as it is resistant to degradation during ethanol-fixed storage (Wong et al., 2012), and degradation can lead to genotyping errors (Pompanon et al., 2005). This sample was therefore preferable to the fin clips for testing and honing the molecular methods (see Figure 2.2), which would later be used with the 2014 samples of C. auratus. The 2013 muscle tissue samples were also used as a positive control in PCR assays (see section 2.3.4) because of the high DNA quality (see

Figure 2.2) and because there was more muscle tissue available from these samples, than from the approximately 12-20 mm cultured juveniles. The muscle tissue collected from

2013 wild adults will hereafter be referred to as ‘muscle tissue.’

Chapter Two – Methods

2.2 DNA extraction

Total genomic DNA was extracted from fish in each sample group using a

MasterPure™ Complete DNA and RNA Purification Kit (Epicentre®). For each extraction a negative control, to which no tissue was added, was run, in order to test for contamination of equipment or reagents with non-target DNA. When work progressed from one sample group to the next, the pipettes were cleaned and calibrated and new reagents prepared in order to avoid contamination of smaller samples with DNA from the preceding sample group i.e. when progressing from larger tissue samples from wild adults to the smaller cultured juveniles.

Total genomic DNA was extracted from ca. 5 mg of ethanol-fixed interstitial fin tissue (wild adults, n = 58) or muscle tissue (muscle tissue, n = 7; cultured juveniles, n = 54). Ethanol was removed from the fixed tissue by suspension in deionized water.

Samples (from individual fish) were then placed in 1.5 mL Eppendorf tubes in a mix of

300 µL Tissue and Cell Lysis Solution and 50 µg of Proteinase K (Promega). Samples were then homogenized and vortexed for 10 seconds before being incubated at 65 ºC for 15 minutes, with samples removed from the incubator and agitated every 5 minutes. If samples were not entirely digested following the initial incubation time, they were incubated for a further 5-10 minutes. Samples were then cooled to room temperature and 10 µg of

RNAse A was added to each sample, to remove any remaining RNA. The samples were incubated at 37 ºC in a water bath, for a further 30 minutes.

Following enzyme digestion, samples were placed in an ice slurry for 5 minutes and

150 µL of cold MPC Protein Precipitate Reagent was added. Samples were vortexed for 10 seconds and then centrifuged for 10 minutes at 11,000 rpm. The resultant supernatant for each sample was placed in a new Eppendorf tube and the protein pellet discarded. While on ice, 500 µL aliquots of cold 100% isopropanol were added to each sample. Samples were inverted approximately 40 times to mix, and centrifuged for 10 minutes at 11,000 rpm to

Chapter Two – Methods precipitate the DNA. Samples were then placed on ice and the isopropanol was decanted, leaving the DNA pellet. The pellet was then washed once with 500 µL of cold, 75% ethanol, which was also decanted. Samples were air-dried with the assistance of vacuum suction for 48-72 hours. The dried DNA was then re-suspended in TE buffer (10 mM Tris-

HCl, 1 mM EDTA, pH 8.0) and stored at -20 °C. The volume of TE buffer added depended on the sample group, and was determined via tests to optimize the concentration of template

DNA for PCR amplification. The final volume of TE buffer added was 70 µL for the wild adults and cultured juveniles and 140 µL for the muscle tissue.

2.2.1 Screening of nuclear DNA

The quality and quantity of template DNA for each extraction was crudely assessed by agarose gel electrophoresis (Figure 2.2). A 10 µL loading mixture was prepared from

2 µL x6 blue/orange load dye (Promega), 6 µL PCR-grade water (Promega) and 2 µL template DNA. This was loaded into wells of a 2% agarose gel (450 mM Tris, 450 mM

Boric Acid, 10 mM EDTA; pH 8.0), stained with 2 µL of SYBR® Safe DNA gel stain

(Invitrogen; 10,000 x concentrate in DMSO). A lambda DNA standard, comprising a 10 µL mixture of 2 µL x6 blue/orange load dye (Promega), 3 µL PCR-grade water (Promega) and

2µL containing 10 µg of uncut lambda DNA (Promega), was included in one well for each origin.

Gels were electrophoresed in a Mini-Sub® Cell GT Cell System (Bio-Rad

Laboratories Pty Ltd) containing TBE buffer (45 mM Tris, 45 mM Boric Acid, 1 mM

EDTA; pH 8.0) at 45 mA, for approximately 20 minutes. Gels were viewed under a UV light using a Molecular Imager® Gel Doc™ XR+ and ImageLab™ software (both Bio-Rad

Laboratories Pty Ltd). Extracts were assessed for fragment degradation and molecular weight, against the lambda standard (Figure 2.2). Only extracts containing high molecular weight template DNA were retained.

Chapter Two – Methods

a) 1 2 3 4 5 6 7 8 b) 1 2 3 4 5 6 7 8 c) 1 2 3 4 5 6 7 8

Figure 2.2: Images of typical agarose gels from samples of a) wild adults, b) cultured juveniles and c) muscle tissue of Chysophrys auratus from Cockburn Sound. Lanes 1-6 contain template DNA, lane 7 contains the negative control and lane 8 contains lambda DNA standard. Saturated pixels are indicated in red.

2.3 Polymerase chain reaction (PCR)

2.3.1 Microsatellite loci

The nDNA analyses were carried out using 11 microsatellite loci (Table 2.1). Ten of these loci (PauA119, PauC102, PauC104, PauC105, PauD104, PauD111, PauD113,

PauD116, PauD117 and PauD118) were developed for C. auratus by Gardner et al. (2014), and the final locus (PMA1) was developed for the closely related Pagrus major by Takagi et al. (1997). The appropriateness of these loci for further analysis was assessed following genotyping with the wild adult sample group (see sections 2.4.1), but before proceeding to

PCR amplification with the cultured juveniles. The wild adults were assayed for all 11 loci whereas, for reasons discussed further in Chapter Three, the cultured juveniles were assayed for all loci, except PauA119 and PauD118.

Chapter Two – Methods

Table 2.1: Characteristics of all 11 microsatellite loci used with Chrysophrys auratus from Cockburn Sound in this study including, for each locus, the GeneBank accession number, locus name, forward (F) and reverse (R) primer sequences and repeat motif. The + sign indicates loci for which the poly-A tail (non-template adenine nucleotide base) was preferentially amplified and scored, rather than the true allele.

GeneBank Locus Primer sequence (5’ to 3’) Repeat motif number

KF408271 PauA119 (+) F: CCACTGATGAATGCCACAAG AC19 R: GCATTGTTTCGCTGGAAGTT

KF408273 PauC102 F: AAATGTCCCTGGGATGTGAG TGTC9 R: CACTGGGAGACCAGAGGAAA

KF408274 PauC104 F: TGGTATCAACAGGAAGA TGTC6.TGCTTG.TC5 R: CTGCCAACTGCCAACTGTA

KF408275 PauC105 (+) F: TCGCTCTTCCTGCTGTGA TGTC8 R: CTGGGTTTCGTCTCTTTTCA

KF408281 PauD104 (+) F: TTAGAAACACCATGCATCTCC TAGA17 R: 5’-GCTGATGGATATTCTGCAGGT

KF408282 PauD111 F: GCGTGGTAACCTTTCATTCC TGGA6TAGA20 R: GTGAGGCACTTTAAGAGCTCAG

KF408283 PauD113 (+) F: CAGCACCATTAAGGTCTGTAAGC CAGT8.CTAC.CTAT11 R: GGATGGATCAGAGCAGGAAA

KF408284 PauD116 F: GCGCCCTCTGTTTTGTAAAG GATG7.GAT.ATAG14 R: CCCGCTGCTCTGATAACCTA

KF408285 PauD117 (+) F: ACCGACTCGTCAAAAATAAGC TAGA15 R: AGCCGATGAACTCCAAACA

KF408286 PauD118 F: GATGAGCCGAATGTGTTG TATC11 R: TCGCCCACTTTACACTGA

AB042989 PMA1 (+) F: CATGCCAGTATTCCAATGTGC GT21 R: AGGACAAATTCCCAAGGTCATCC

2.3.2 PCR amplification

Polymerase chain reaction (PCR) was used to amplify the 11 and nine microsatellite loci from the DNA extracts of, respectively, the wild adult and cultured juvenile sample groups (Table 2.1). PCR products were directly labeled with fluorescent, 6-FAM labels

(Geneworks™). A positive sample, i.e. a DNA extract from muscle tissue which amplified consistently, was added to each set of PCR assays (a group of reactions done at the same time) to test for the presence of PCR artefacts. Additionally, a negative sample, i.e. a sample to which no template DNA was added, was also included, to test for contamination with non-target DNA.

Chapter Two – Methods

Each PCR assay contained 0.4 M of each forward and reverse primer (see Table

2.1), 0.001 U of Taq polymerase (Roche), 1 mM reaction buffer (10 mM Tris-HCl, 1.5 mM

MgCl2, 50 mM KCl, pH 8.3, Roche), 0.4 mM of dNTPs mix, containing each dNTP

(Promega), and approximately 10 ng of template DNA, and was made up to a final reaction volume of 25 µL with the addition of PCR-grade water (Promega). PCR assays were conducted in a Veriti 96 Well Thermal Cycler (Applied Biosystems) with touchdown cycling parameters, according to Gardner et al. (2014).The touchdown procedure consisted of an initial denaturation phase at 94°C for 2 min, followed by 24 step-wise amplification cycles at 94°C for 30 sec, 62°C (decreased by 0.5°C per cycle) for 30 sec and 72°C for 60 sec; and a further 25 amplification cycles at 94°C for 30 sec, 50°C for 30 sec and 72°C for

60 sec, followed by a final extension step at 72°C for 5 min. Samples were then removed from the thermal cycler and stored at -20ºC.

2.3.3 Screening of PCR product

The quality and quantity of the PCR product for each locus was crudely assessed for a subset of wild adults and cultured juveniles (usually five fish from each sample group) and a positive and negative control using agarose gel electrophoresis. The protocol for agarose electrophoresis follows that detailed in section 2.2.1, with the following exceptions: a 12 µL loading mixture was prepared from 2 µL x6 blue/orange load dye (Promega) and

10 µL PCR product. The PCR standard was a 12 µL mixture of 2 µL x6 blue/orange load dye (Promega), and 10 µg of Phi-X174 DNA/Hae III Marker (Promega). Gels were electrophoresed at 44 mA, for approximately 20 minutes.

2.3.4 Fragment length analysis

PCR products were screened using capillary electrophoresis and laser detection on a

3,730 DNA Analyzer (Applied Biosystems) operated by Murdoch University State

Agricultural and Biosecurity Centre (SABC) technical staff. The products of two loci for a single individual were sometimes loaded into the same well of the Fisher Biotec 96-well

Chapter Two – Methods plate, in combinations listed in Table 2.2. Products were loaded in this manner where, based on the results of Gardner (Murdoch University, unpublished data), the expected fragment lengths for the two loci were non-overlapping. Each plate contained PCR product from multiple assays and the corresponding negative and positive (muscle tissue, of known fragment size) control samples were included on the same plate. The control samples were used to check for contamination and scoring inconsistencies across plates. The loading mixture for each well had a total volume of 15 µL, containing 14.85 L of Hi-DiTM

Formamide and 0.15 L of GeneScan LIZ 600 v 2.0 size standard (both Applied

Biosystems) and 1 µL of FAM-labeled PCR product per locus. The loaded plate was subsequently sealed with a septum.

Table 2.2: Combinations of locus pairs loaded into the same wells of Fisher Biotec 96-well plates for capillary electrophoresis.

Locus 1 Locus 2 PauD117 PauD118 PauA119 PauC102 PauD116 PMA1 PauC104 PauC105 PauD111 PauD104 PauD113 -

The raw electropherograms produced by capillary electrophoresis were viewed using GeneMarker® software v 1.95 (SoftGenetics Inc.). The fragment lengths for each allele were manually scored, for alleles whose relative fluorescence units (RFUs) were

> 1,000. Where the RFUs were less than this value, the PCR assay was re-run, before plating again. The raw assigned genotypes were recorded in a MS Excel™ spreadsheet.

Following genotyping, a random sub-set of approximately 10% of individuals at each locus, within each sample group, were blindly re-scored. Where any of these scores differed from the initial score, all samples from the plate from which the inconsistent genotype originated were re-scored, until the assigned genotypes were consistent.

Chapter Two – Methods

2.4 Data analyses

2.4.1 Locus selection

Preliminary analyses to determine the reliability of all 11 loci for use in further analyses are detailed in this section. These analyses were carried out with all 11 loci for the sample of wild adult fish, before proceeding to the sample of cultured juvenile fish

(hereafter, ‘samples’). This was done to assess whether each of these loci could be reliably scored, before proceeding to the culture juveniles, to ensure resources were not wasted on unreliable loci which could introduce biases into the dataset. In the following sections, unless otherwise stated, analyses were conducted using the nine loci (PauC102, PauC104,

PauC105, PauD104, PauD111, PauD113, PauD116 and PauD117) assayed for both samples.

2.4.1.1 Hardy-Weinberg equilibrium

In order to determine whether the observed heterozygosity at each locus, in each sample (HO) differed significantly from the expected heterozygosity (HE) under Hardy-

Weinberg equilibrium (sensu Hardy, 1908), exact tests, after Guo and Thompson (1992) were used. This test was implemented in GENEPOP v 4.2 on the web (Raymond and

Rousset, 1995; Rousset, 2008) and employed Markov chain resampling (10,000 dememorization steps, 1,000 batches, 10,000 iterations per batch) to calculate the unbiased estimate of the P-value. This procedure was applied to each sample separately: for all 11 loci with the wild adults and for nine loci with the cultured juveniles. Prior to analysis in

GENEPOP, the program CONVERT v 1.31 (Glaubitz, 2004) was used to convert the raw genotype data into the GENEPOP format.

The step-down procedure of a Holm-Bonferroni sequential correction (Holm, 1979) was used to correct for the inflated risk of Type I error associated with multiple, simultaneous hypothesis tests. The probability of Type I error (falsely rejecting a true null hypothesis, α), increases with increasing number of tests (k) according to 1− (1 − 훼)푘

Chapter Two – Methods

(Narum, 2006). This particular procedure was selected over the traditional Bonferroni correction, which is often employed in this context, because of its greater accuracy, power and versatility (Ludbrook, 1998). This procedure was applied to each sample separately, in order to adjust the α for each of the multiple hypothesis tests.

Wright’s fixation index, FIS (Wright, 1951), also known as the inbreeding coefficient (Weir and Cockerham, 1984), which is the rate of inbreeding in an individual relative to the sub-population from which it was drawn, was also calculated. This was calculated for each locus (11 with the wild adults, nine with the cultured juveniles), within each sample, in FSTAT v 2.3.9.2 (Goudet, 1995, 2002). Values for FIS range from +1, indicating heterozygote deficiency, to -1, which indicates heterozygote excess. The value of the coefficient indicates the directionality of deviations from Hardy-Weinberg equilibrium

(Balloux and Lugon-Moulin, 2002; Weir and Cockerham, 1984).

2.4.1.2 Null alleles

To assist in the interpretation of the results of the Hardy-Weinberg tests, the allele and genotype data for all 11 loci with the wild adults and nine loci with the cultured juveniles, were assessed for evidence of null alleles, large allele dropout, scoring of stutter peaks and typographic (data input) errors. This was done using MICRO-CHECKER v 2.2.3 with 1,000 Monte Carlo randomizations (Van Oosterhout et al., 2004). Where MICRO-

CHECKER identified the presence of null alleles, the algorithms of Van Oosterhout et al.

(2004), Chakraborty et al. (1992) and Brookfield (1996) were implemented in MICRO-

CHECKER to estimate the frequency of these alleles. In addition, the maximum likelihood function of Kalinowski and Taper (2006) was implemented in the program ML-RELATE

(Kalinowski et al., 2006). The mean null allele frequency across all four algorithms was calculated in MS Excel™.

Chapter Two – Methods

2.4.1.3 Linkage disequilibrium

Evidence of non-random associations of genotypes between loci (linkage disequilibrium) was assessed using log likelihood ratio statistics (G-tests) and Markov chain resampling (10,000 dememorization steps, 1,000 batches and 10,000 iterations per batch) between all possible locus pairs. This was carried out according to the equations reported in

Raymond & Rousset (1995b), as implemented in GENEPOP v 4.2 on the web (Raymond and Rousset, 1995; Rousset, 2008). This procedure was initially applied to the two samples separately: for all 11 loci with the wild adults and nine loci with the cultured juveniles, and then with nine loci for the two samples combined. Where both samples were assessed, a global test using Fisher’s method was performed in GENEPOP for each locus pair. Since there were n (n − 1)/2 possible pairs of loci (where n is the number of loci) to be tested, this therefore carries an inflated risk of Type I error. Consequently, the sequential Bonferroni-

Holm correction (Holm, 1979) was employed to assess the significance of results.

2.4.2 Reliability of genotype scoring

2.4.2.1 Fragment length analysis

In order to determine the reliability of the manually-assigned fragment lengths for each allele/genotype, a random subset of 10% of individuals at each locus, within each sample, was blindly scored by the author and a scorer experienced with these loci. Prior to independent, blind scoring, the methods for calling allele sizes were standardised between the two scorers, for each locus. A measure of the mean scoring error rate per allele, and per locus in each sample, was calculated by comparing the genotypes assigned by the two scorers. The rate of occurrence of errors (lack of agreement between scorer-assigned genotypes) was calculated according to equations (1) and (2) in Pompanon et al. (2005).

2.4.2.2 Sample size

To test for evidence of insufficient sampling, rarefaction curves plotting sample size

(K) against mean number of alleles per locus (A) were produced using the function

Chapter Two – Methods

‘jackmsatpop’ from the package PopGenKit (Paquette, 2012), as implemented in the statistics package R (R Core Team, 2013). A sampling interval of one was used and 1,000 jackknife replicates per sampling interval were employed for each locus, for the number of individuals sampled in the wild adult and cultured juvenile samples. This test was performed because genetic analyses can be sensitive to errors associated with sample sizes being insufficient to capture the population-wide diversity at highly polymorphic markers

(Allendorf et al., 2013; Waples and Do, 2010).

2.4.3 Genetic diversity per locus

Genetic variation was measured at 11 loci for the wild adult sample and nine loci for the cultured juvenile sample by computing the number of alleles per locus (A) and the geneic diversity per locus, as measured by Nei's (1978) unbiased estimate of expected heterozygosity (HE) and observed heterozygosity (HO). These parameters were calculated using the MS Excel™ Add-In GenAlEx v 6.5 (Peakall and Smouse, 2006, 2012). Because the number of observed alleles (A) is sensitive to sample sizes (Allendorf et al., 2013), the unbiased allelic richness (AR), which is a standardized measure of the number of alleles per locus, independent of the sample size, was calculated for each locus. A rarefaction method, which has the highest precision of methods to standardize the number of alleles per locus

(Leberg, 2002), was implemented in FSTAT v 2.3.9.2 (Goudet, 1995, 2002). In addition to calculating these parameters per locus, the mean per population was also calculated using

MS Excel™.

2.4.4 Comparison of genetic composition between cultured juveniles and wild adults

Statistical tests for differences in the observed number of alleles (A), allelic richness

(AR), expected heterozygosity (HE) and inbreeding coefficient (FIS) between the cultured juvenile and wild adult samples were conducted in the statistical analysis package, SPSS

Statistics v 23.0 (IBM). A nonparametric sign test for a difference between related samples, the Wilcoxon signed-rank test, with one-sided probabilities (cultured juveniles < wild

Chapter Two – Methods

adults), was employed for the inbreeding coefficient (Figure 2.3a). However, with the

parameters: A, AR and HE, the distributions of the observations about the median were

highly asymmetrical (Figure 2.3b), thus violating the assumption of roughly symmetrically-

distributed observations required for the Wilcoxon signed-rank test (Zar, 2010).

Consequently, it was necessary to employ a far less powerful nonparametric test, the sign

test (also one-sided), for these parameters.

0.15 a) 1.0 b)

) 0.10 IS 0.8

(

)

E 0.05 H 0.6 0.00 0.4 -0.05 ding coefficient Heteozygosity ( 0.2 -0.10 Inbree -0.15 0.0

Wild adults Cultured juveniles Wild adults Cultured juveniles

A test for genic differentiation between the wild adults and cultured juveniles, i.e. a

test for heterogeneity in the allelic compositions, and the frequencies of those alleles, at

each locus, was carried out using G-tests. These were implemented in GENEPOP v 4.2 on

the web (Raymond and Rousset, 1995; Rousset, 2008) and employed Markov chain

resampling (10,000 dememorization steps, 1,000 batches, 10,000 iterations per batch).

Additionally, a global test for genic differentiation, using Fisher’s method, was performed

across all loci, also in GENEPOP. The allele frequencies at each locus were also calculated

for both samples using GenAlEx v 6.5 (Peakall and Smouse, 2006, 2012). The observed

numbers of private alleles (alleles unique to one sample) in each sample were determined

from this data. A nonparametric sign test (one-tailed) was carried out to determine whether

the cultured juveniles had significantly few private alleles than the wild adults.

Chapter Two – Methods

2.4.5 Bottleneck

A heterozygosity excess method (Cornuet and Luikart, 1996) was used to test whether the cultured juveniles had experienced a recent reduction in effective population size (Ne), i.e. a bottleneck, during the culturing process, including the sampling of eggs. For comparative purposes, this method was also used to test for evidence of a bottleneck in the wild adults. This analysis was conducted with the program BOTTLENECK v 1.2.02

(Cornuet and Luikart, 1996; Cristescu et al., 2010).

The heterozygosity excess method is based on the fact that although allelic richness decreases rapidly following a bottleneck, due to the loss of rare alleles, there is little effect on heterozygosity (Allendorf, 1986; Cornuet and Luikart, 1996). This method therefore compares the expected heterozygosity at each locus (HE) to the heterozygosity expected, given the observed number of alleles and sample size (HExp), under three models of mutation-drift equilibrium. These models were the step-wise mutation model (SMM), the infinite alleles model (IAM) and the two-phase model (TPM), which combines the former two (Luikart and Cornuet, 1998; Piry et al., 1999).

Simulations in BOTTLENECK were run for both samples under all three models, with a value of 12 for the size of variance in multistep mutations and using 1,000 iterations.

For the two-phase model, the relative contributions of the IAM and SMM were set at 95% and 5% respectively, as recommended by Piry et al., (1999). A nonparametric sign test was used to determine the statistical significance of the number of loci for which a heterozygosity excess was detected. Furthermore, BOTTLENECK was also used to test for a mode-shift in allele frequencies (Piry et al., 1999), i.e. to determine whether the mode of allele frequencies ‘shifted’ from low, to intermediate frequency classes, indicating a significant loss of rare alleles characteristic of a bottleneck (Luikart et al., 1998).

Chapter Two – Methods

2.4.6 Inbreeding and relatedness

The mean heterozygosity and mean inbreeding coefficient for each sample was compared between the cultured juveniles and wild adults to determine the rate of increase in homozygosity due to inbreeding, in each sample. Additionally, relatedness (r) between pairs of individuals in the wild adult and cultured juvenile samples, based on their multi-locus genotypes, was assessed using a maximum likelihood estimation approach implemented in

ML-RELATE (Kalinowski et al., 2006). This approach was selected because it is more accurate than other relatedness methods (Milligan, 2003). The mean relatedness for each sample was calculated in MS Excel™.

The maximum likelihood relationships between each pair of individuals, in each sample, were also calculated using ML-RELATE. The program assigns each pair of individuals to one of four common pedigree relationships: unrelated (U), half-sibs (HS), full-sibs (FS), or parent–offspring (PO). The relative proportions of pairs assigned to each

(most likely) relationship were calculated in MS Excel™. Because the maximum likelihood method ignores the probability of the other (less likely) relationships, to determine the confidence in the assigned (most likely) relationship the mean of the combined posterior probabilities of each relationship type, for each pair of individuals, was calculated. This was done for each sample using equation 3 from Kalinowski et al. (2006) and assuming neutral priors. These calculations were executed in MS Excel™.

2.4.7 Effective population size

Single-sample linkage disequilibrium (LD) methods for estimating the inbreeding effective population size in each sample were implemented in two programs: i) LDNe v

1.31, which employs Burrow’s Δ statistic and a bias correction for sample size (Waples and

Do, 2008), and ii) ONeSAMP, which uses approximate Bayesian computation and eight test statistics (Tallmon et al., 2008). With LDNe, low frequency alleles can bias results. Alleles of < 0.02 frequency were therefore excluded from analysis, as this approach provides a

Chapter Two – Methods good balance between maximizing precision and minimizing bias (Waples and Do, 2010).

A jackknifing procedure was employed to produce unbiased 95% confidence intervals for the point estimate of Ne (Waples and Do, 2008).

The performance of ONeSAMP is dependent on the upper and lower bounds of the prior on the Ne, which are defined by the user. Without prior information on the expected Ne for either Cockburn Sound population, the temporal results of Hauser et al. (2002) for

C. auratus in New Zealand were used to inform the choice of priors here. Runs with different priors ranging from 20 < Ne < 10,000 were conducted to assess the sensitivity of results. Tests with five different sets of priors, with upper limits ranging from 500- 10,000 were conducted for the wild adults. Unfortunately, due to a server issue with the ONeSAMP website, only three runs were conducted with the cultured juveniles, with upper limits ranging from 2,500-10,000. For each run, the 95% confidence intervals were calculated for the mean Ne estimated by ONeSAMP. The mean estimate across all the runs conducted for each sample was also calculated.

Chapter Three – Results

3.1 Locus selection

3.1.2 Hardy-Weinberg Equilibrium

Preliminary analyses of the patterns of variation at all 11 microsatellite loci in the sample of wild adults were conducted. Exact tests for Hardy-Weinberg equilibrium (HWE) revealed that the patterns of variation at nine loci were in accordance with HWE, indicating that these loci were likely free of null alleles and other PCR artefacts. This was not the case, however with two loci, PauA119 (P < 0.01) and PauD118 (P < 0.01), whose genotype proportions were significantly different from those expected under HWE (Table 3.3). These deviations from HWE remained significant following sequential Holm-Bonferroni correction. A deficit in heterozygosity, as indicated by the large, positive inbreeding coefficients (FIS) at PauA119 (0.140) and PauD118 (0.173), was likely the source of the observed departures from HWE (Table 3.3). The source of these deviations was investigated further in MICRO-CHECKER (see below). The remaining nine loci were also examined for departures from HWE in the cultured juvenile sample, and all were found to be in accordance with equilibrium expectations.

3.1.2 Null alleles

Preliminary evaluations of assigned genotypes with MICRO-CHECKER did not identify any scoring errors due to stuttering or large allele dropout at any of the 11 loci tested with the wild adult sample. However, MICRO-CHECKER did identify the potential presence of null alleles, i.e. alleles that fail to amplify, at locus PauA119 (Fisher’s combined probability test, P > 0.05) and PauD118 (P < 0.01), as indicated by the excess of homozygotes across most allele size classes (Figure 3.1).

54 Chapter Three –Results

7 a)

6 5 4 3 2 1

Numberhomozygotes of 0 219 223 227 231 235 239 243 247 251 255 259

b) 6 5 4 3 2

Numberhomozygotes of 1 0

280 284 288 292 296 300 304 308 312 316 320

Allele size (bp) Figure 3.1: Results from MICRO-CHECKER, indicating the expected homozygote allele frequencies (•) and 95% confidence intervals and observed homozygote frequencies (x) for the loci a) PauA119 and b) PauD118, with wild adults of Chrysophrys auratus from Cockburn Sound.

The estimated frequency of null alleles was generally congruent across the four algorithms employed. They indicate that the frequency of null alleles at PauA119 (mean =

0.066) and PauD118 (mean = 0.082) were generally low (Table 3.1). Nevertheless, because the presence of null alleles can bias estimates of allele frequencies and genetic variation, and given the combination of deviations from HWE and evidence of null alleles at these loci, a conservative approach was taken, where these loci were excluded from further analysis in the cultured juvenile sample. There was no evidence of null alleles at any of the remaining nine loci, in either of the two samples. In reference to further analyses, unless otherwise stated, analyses were conducted using nine loci for both the wild adults and cultured juveniles.

Chapter Three –Results

Table 3.1: Null allele frequencies at the loci PauA119 and PauD118, determined by MICRO-CHECKER using the algorithms of Van Oosterhout et al. (2004), Chakraborty et al. (1992) Brookfield (1996), and using the null allele frequency estimator in ML-RELATE (Kalinowski et al., 2006). The mean estimate of null allele frequencies for all four algorithms (± standard error) is also indicated.

Locus Van Chakraborty Brookfield Kalinowski, Mean estimate Oosterhout et et al. (1992) (1996) Wagner & Taper al. (2004) (2006) PauA119 0.065 0.071 0.063 0.064 0.066 ±0.00 PauD118 0.079 0.090 0.077 0.084 0.082 ±0.00

3.2.3 Linkage Disequilibrium

Log-likelihood ratio (G-tests) for linkage disequilibrium between pairs of loci produced a significant result between PauD117 and PauD104 (P = 0.0033) in the wild adult sample and for the two samples combined with Fisher’s method, P = 0.0223 (Table 3.2).

Following sequential Holm-Bonferroni correction for the 36 pair-wise tests, none of the pairs of loci were found to be in linkage disequilibrium. Therefore, the genotype proportions at each locus were largely concordant with those expected under gametic equilibrium. Thus, the patterns of variation at each locus were assumed to be independent of one another and none of the remaining nine loci needed to be excluded from further analysis due to linkage disequilibrium.

Chapter Three –Results

P-value of G-test Global test (Fisher’s Method)

Wild adults Cultured Chi-Square (χ2) df P-value Locus pair juveniles test statistic

PauD113 & PauD117 1.0000 1.0000 0.0000 4 1.0000 PauD113 & PauD104 1.0000 1.0000 0.0000 4 1.0000 PauD117 & PauD104 0.0033 1.0000 11.4156 4 0.0223 PauD113 & PauC102 0.6253 0.4665 2.6106 4 0.6249 PauD117 & PauC102 0.6319 1.0000 0.9180 4 0.9220 PauD104 & PauC102 0.3740 1.0000 1.9673 4 0.7418 PauD113 & PauC104 0.8315 0.9816 0.4062 4 0.9820 PauD117 & PauC104 0.4855 0.3446 3.5563 4 0.4694 PauD104 & PauC104 0.8889 0.2336 3.1368 4 0.5352 PauC102 & PauC104 0.5859 0.6716 1.8648 4 0.7606 PauD113 & PauC105 0.7956 0.6399 1.3642 4 0.8504 PauD117 & PauC105 0.5317 0.2427 4.1718 4 0.3833 PauD104 & PauC105 0.3466 0.5004 3.4896 4 0.4795 PauC102 & PauC105 0.6829 0.0706 6.0955 4 0.1921 PauC104 & PauC105 0.5678 0.2958 3.5777 4 0.4662 PauD113 & PauD111 0.4010 1.0000 1.8274 4 0.7675 PauD117 & PauD111 1.0000 0.2612 3.1391 4 0.5348 PauD104 & PauD111 0.5840 1.0000 1.0757 4 0.8981 PauC102 & PauD111 0.4685 0.1437 5.4232 4 0.2466 PauC104 & PauD111 0.8243 0.6998 1.1159 4 0.8917 PauC105 & PauD111 0.7894 0.5166 1.7866 4 0.7749 PauD113 & PauD116 1.0000 1.0000 0.0000 4 1.0000 PauD117 & PauD116 1.0000 0.2235 2.8928 4 0.5759 PauD104 & PauD116 1.0000 1.0000 0.0000 4 1.0000 PauC102 & PauD116 0.0424 0.5831 7.4345 4 0.1146 PauC104 & PauD116 0.3884 0.3288 4.1372 4 0.3878 PauC105 & PauD116 0.8590 0.0196 8.2680 4 0.0822 PauD111 & PauD116 1.0000 0.2394 2.6777 4 0.6131 PauD113 & PMA1 0.8366 0.5124 1.7287 4 0.7855 PauD117 & PMA1 0.9572 0.0382 6.8331 4 0.1450 PauD104 & PMA1 0.6081 0.7133 1.6896 4 0.7926 PauC102 & PMA1 0.8513 0.6784 1.0914 4 0.8956 PauC104 & PMA1 0.7690 0.9767 0.5720 4 0.9661 PauC105 & PMA1 0.5463 0.5173 2.5483 4 0.6360 PauD111 & PMA1 0.5541 0.0815 6.3403 4 0.1751 PauD116 & PMA1 0.1162 0.7463 4.9353 4 0.2940 * P-values were only considered significant, when they remained significant following a sequential Holm-Bonferroni correction for 36 pair-wise comparisons.

Chapter Three –Results

3.2 Reliability of genotype scoring

3.2.1 Fragment length analysis and sample size

Across the nine loci, each manually-assigned genotype was in perfect agreement between the two scorers, i.e. no scoring errors were detected (data not shown). This therefore gives confidence that the assigned genotypes (see Appendix 1) reflect the true genotypes for each individual.

Rarefaction methods indicated that the sample sizes for wild adults (n = 58) and cultured juveniles (n = 54) were sufficient to capture the allelic diversity present at most loci. Specifically, the curves at most loci reached, or at least approached, an asymptote for

(A) within the sample sizes for the wild adults and cultured juveniles (Figure 3.2). The exceptions were the relationships observed at PauD113 (A = 19) and PauD117 (A = 23) in the wild adults and PauD117 (A = 21) in the cultured juveniles (Figure 3.2; Table 3.3). This suggests that further sampling at these highly polymorphic loci would likely have resulted in a greater number of alleles being detected.

3.3 Genetic diversity per locus

Genotypes were determined for 58 wild adults and 54 cultured juveniles at nine loci.

The raw genotype data for each individual, at each of the nine loci that were assayed for both samples, have been provided as a resource for future research (see Appendix 1). For both samples pooled, a total of 122 alleles were detected across the nine loci (Table 3.3).

The observed number of alleles (A) per locus for the nine loci across both samples, ranged from low to high. Specifically, the observed number of alleles ranged from 4 (PauC104) to

26 (PauD117), with a mean of 13.56 ±2.43. The mean allelic richness (AR) per locus, which was based on a minimum sample size of 54, ranged from 3.47 (PauC104) to 21.81

(PauD117) for the nine loci (Table 3.3). The mean expected (HE) and observed heterozygosity (HO) per locus also ranged from low to high, with a minimum at locus

PauC104 of 0.158 and 0.160 respectively, to a maximum at locus PauD113 of 0.913 and

Chapter Three –Results

0.937, for the nine loci (Table 3.3). The moderate to high levels of polymorphism detected provide sufficient discriminatory power to conduct meaningful comparisons between the genetic compositions of the wild adults and cultured juveniles.

25 a)

n=58

) A

10 Number Number alleles of ( 5

0 1 5 9 13 17 21 25 29 33 37 41 45 49 53 57

25 b) n=54

) A

10 Number alleles Number of ( 5

0 1 5 9 13 17 21 25 29 33 37 41 45 49 53

Sample size (K)

PauD113 PauD117 PauD104 PauC102 PauD111 PauD116 PMA1 PauC105 PauC104

Chapter Three –Results

Locus Diversity Wild adults Cultured juveniles P-value of G- parameter test - - PauA119 N 58 - A 18 - AR 17.73 - HE 0.913 - HO 0.793 - FIS 0.140 - P 0.0012* PauC102 N 58 54 0.1400 A 13 12

AR 12.79 12.00

HE 0.852 0.867

HO 0.879 0.963

FIS -0.023 -0.102 P 0.2502 0.1067

PauC104 N 58 54 0.8313

A 4 3

AR 3.93 3.00

HE 0.177 0.139

HO 0.172 0.148

FIS 0.033 -0.055 P 0.4092 1.0000

PauC105 N 58 54 0.3194 A 4 4

AR 3.93 4.00

HE 0.541 0.476

HO 0.586 0.519

FIS -0.074 -0.080 P 0.9071 0.8352

PauD104 N 58 54 0.0822

A 14 14

AR 13.92 14.00

HE 0.889 0.910

HO 0.897 0.907

FIS 0.000 0.012 P 0.6425 0.8082 PauD111 N 58 54 0.4219

A 13 16

AR 13.00 16.00

HE 0.897 0.907

HO 0.897 0.889

FIS 0.009 0.029 P 0.0566 0.4190 * Statistically significant results following a sequential Holm-Bonferroni correction are indicated in bold, with significant values denoted by * and very significant values denoted by **. The symbol (-) indicates that data were not collected at this locus, for this sample. 60

Chapter Three –Results

Table 3.3 continued: Locus characteristics for wild adults and cultured juveniles of Chrysophrys auratus from Cockburn Sound. Where N is the number of individuals sampled at each locus, A is the observed number of alleles, AR is the allelic richness, which represents the number of observed alleles independent of sample size, HE is the unbiased expected heterozygosity, HO is observed heterozygosity, FIS is Wright’s inbreeding coefficient and P is the resultant P-value from exact tests for Hardy-Weinberg equilibrium. The P-value of G test denotes exact probability of a Type I error for the null hypothesis that the allelic compositions and their frequencies at each locus in wild adult and cultured juvenile samples are the same.*

Locus Diversity parameter Wild adults Cultured juveniles P-value of G- test

PauD113 N 58 54 0.0904

A 19 16

AR 18.71 16.00 HE 0.917 0.909

HO 0.948 0.926

FIS -0.025 -0.010 P 0.1125 0.7480

PauD116 N 58 54 0.0159

A 13 16

AR 12.92 16.00 HE 0.883 0.900

HO 0.897 0.907

FIS -0.007 0.001 P 0.6717 0.4275 PauD117 N 58 54 0.7917

A 23 21

AR 22.63 21.00

HE 0.917 0.904

HO 0.897 0.926

FIS 0.031 -0.015 P 0.1669 0.3941 PauD118 N 58 - -

A 11 -

AR 10.82 - HE 0.867 -

HO 0.724 -

FIS 0.173 - P 0.0000** - PMA1 N 58 54 0.0480

A 5 5

AR 5.00 5.00

HE 0.576 0.636

HO 0.517 0.611

FIS 0.111 0.049 P 0.5544 0.5983

* Statistically significant results following a sequential Holm-Bonferroni correction are indicated in bold, with significant values denoted by * and very significant values denoted by **. The symbol (-) indicates that data were not collected at this locus, for this sample.

Chapter Three –Results

3.4 Comparisons of genetic composition between cultured juveniles and wild adults

The mean number of alleles (± standard error) detected in the cultured juveniles

11.89 ±2.13, was very similar to that in the wild adult sample, with a mean of 12.00 ±2.22

(Table 3.3). Furthermore, there was no consistent pattern regarding the number of alleles present at individual loci, with either of the two samples. For example, the number of alleles was the same between the cultured juveniles and wild adults at PauC105 (A = 4), PauD104

(A = 14) and PMA1 (A = 5). While three fewer alleles were found in the cultured juveniles than wild adults at PauD113, three more alleles were present in the cultured juveniles than the wild adults at both PauD111 and PauD116 (Table 3.3). The number of alleles did not differ significantly between the two samples (P = 0.3440) for the one-tailed (cultured juveniles < wild adults) sign test (Tables 3.3; 3.4). When the number of alleles was standardized to a minimum sample size of 54, the mean allelic richness (AR) did not differ significantly between cultured juveniles (11.89 ±2.13) and wild adults (11.87 ±2.18). The similarity between the allelic richness of the two samples was supported by the non- significant result (P = 0.5000) for the sign test (Tables 3.3; 3.4).

Levels of genic diversity (HE) did not differ significantly between the two samples

(sign test, P = 0.5000), which both had a mean expected heterozygosity of 0.739 ±0.09

(Tables 3.3; 3.4). The mean inbreeding coefficient, FIS in the cultured juveniles was -0.019

±0.02, which more negative, but still similar to the mean for the wild adults, 0.006 ±0.02.

This suggests that the cultured juveniles had a slight tendency towards heterozygote excess, while the wild adults tended to not exhibit either heterozygote or homozygote excess (Table

3.3). Despite the apparent differences between the mean inbreeding coefficients of the two samples, the result of a Wilcoxon-signed rank test (P = 0.1800) was non-significant (Table

3.5).

Chapter Three –Results

Diversity parameter

A AR HE FIS

One-tailed P-value 0.3440 0.5000 0.5000 0.1800

Significant differences in allelic composition and frequencies (together, the distribution), between the cultured juvenile and wild adult samples, were observed at

PauD116 (G-test, P = 0.0159) and PMA1, P = 0.0480 (Figure 3.3; Table 3.3). However, following sequential Holm-Bonferroni correction, the results were found to be non- significant for all loci. Nevertheless, a global Fisher’s test across all loci returned a test statistic (χ2) of 32.93 and a significant P-value (0.0170), suggesting that the differences at each individual locus, while small, when considered together, represent significantly different allelic distributions between the two samples (Table 3.3). The differences between the cultured juveniles and wild adults were generally the a consequence of the presence of private alleles (alleles unique to one sample) at the extremities of the allele size range

(Figure 3.3).

In the cultured juvenile population, 14 of the 107 alleles detected (13.1%) were private alleles. Similarly, of the 108 alleles detected in the wild adults, 15 of these were private alleles, i.e. 13.9% (Table 3.5). The frequencies of these private alleles ranged from

0.009 – 0.056, indicating that they are rare alleles. The number of private alleles per locus ranged from zero to four, with a mean of 1.56 ±0.47 for the cultured juveniles, and ranged from zero to five, with a mean of 1.67 ±0.50 for the wild adults (Table 3.5). The cultured juveniles possessed fewer private alleles for three of the nine loci, however this difference was not significant for the one-tailed sign test, P = 0.2540 (Table 3.5).

Chapter Three –Results

Locus Wild adults Cultured juveniles PauC102 2 1 PauC104 1 0 PauC105 1 1 PauD104 1 1 PauD111 1 4 PauD113 3 0 PauD116 0 3 PauD117 5 3 PMA1 1 1 Mean 1.67 ±0.50 1.56 ±0.47

242 a) b)

138 232

222 136

212

134 202

Allele fragment fragment Allele size (bp) 192 132

182 130 172

162 128 Figure 3.3: Composition and frequency (indicated by bubble size) of alleles for wild adults and cultured juveniles of Chrysophrys auratus from Cockburn Sound at the loci a) PauD116 and b) PMA1, which exhibited significant genic differentiation prior to Holm-Bonferroni correction.

Chapter Three –Results

3.5 Bottleneck

In testing for a recent bottleneck, neither the cultured juveniles, nor the wild adults displayed a significant (sign test, P > 0.05) heterozygosity excess under any of the three mutation-drift models (Table 3.6). However, the estimate for the cultured juveniles under the IAM model did approach the level expected for statistical significance (P = 0.0670).

Nonetheless, because this only occurred under one model, it was not considered to be indicative of a bottleneck. Additionally, a similar result was obtained for the wild adults

(P = 0.0641; Table 3.6), indicating that the near-significant result was not the result of a bottleneck induced by the culturing process.

Sample Sign Test Allele frequency distribution IAM SMM TPM

Wild adults 0.0641 0.2960 0.3009 L-shaped distribution Cultured juveniles 0.0670 0.5452 0.5474 L-shaped distribution

The analysis of allele frequency distributions also revealed a typical L-shaped distribution for both sample groups (Figure 3.4), which is expected for neutral loci under mutation-drift equilibrium. Therefore, there is no evidence to suggest the culturing process resulted in a bottleneck in the effective size of the cultured juveniles.

Chapter Three –Results

0.8

0.7

0.6

0.5

0.4

0.3 Proportion of alleles of Proportion 0.2

0.1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Allele frequency class

Figure 3.4: Histogram of allele frequencies produced by the program BOTTLENECK for nine loci with the wild adult and cultured juvenile samples of Chrysophrys auratus from Cockburn Sound.

3.6 Inbreeding and relatedness

The mean inbreeding coefficient was not significantly different from zero for the wild adults or cultured juveniles, indicating inbreeding was not appreciable in either sample

(Table 3.3). The mean pairwise relatedness (r) between individuals was similar for both samples, with a mean relatedness of 0.044 ±0.00 for the cultured juveniles and 0.046 ±0.00 for the wild adults (Figure 3.5b; Table 3.7). These values indicated that, on average, individuals were less closely related than first-cousins (r < 0.07), i.e. they were unrelated.

Sample Proportion U Proportion HS/FS Mean posterior Mean probability, U relatedness (r) Wild adults 0.883 0.116 0.788 ±0.01 0.046 ±0.00 Cultured 0.883 0.115 0.793 ±0.01 0.044 ±0.00 juveniles

The proportion of unrelated pairs, based on maximum likelihood estimates of pairwise relatedness between individuals, was very similar between the cultured juveniles and wild adults. This method determined that most pairs of individuals (88.3%) in each of

Chapter Three –Results the two samples, were more likely to be unrelated than either half-sibs or full-sibs (Table

3.7). Additionally, the mean of the posterior probabilities for unrelated relationships were also similar: 0.793 ±0.01 for the cultured juveniles and 0.788 ±0.01 for the wild adults

(Figure 3.5a; Table 3.7). This indicates similar levels of confidence in the maximum likelihood assignment of an unrelated relationship, i.e. the other relationships were proportionally unlikely, compared to the likelihood of an unrelated relationship between pairs of individuals.

0.805 a) 0.048 b) 0.047 0.800 0.046 0.795 0.045 0.790 0.044 0.043

Probability 0.785

Mean relatedness (r) relatedness Mean 0.042 0.780 0.041 0.775 0.040 0.770 0.039

Figure 3.5a) Mean of the posterior probabilities ± standard error, for unrelated relationships and b) mean relatedness (r) ± standard error, produced in the program ML-RELATE, between wild adult and cultured juvenile samples of Chrysophrys auratus from Cockburn Sound.

3.7 Effective population size

The estimates obtained from LDNe and ONeSAMP for the inbreeding effective number of breeders (NbI) for the cultured juvenile sample were similar and had overlapping confidence intervals. LDNe returned a point estimate of 203 with 95% confidence intervals of 106-1,087 (Table 3.8), while the mean estimates of NbI with ONeSAMP ranged from

133-173, with a mean of 151, and 95% confidence intervals ranging from 110-403. The confidence intervals for ONeSAMP are quite narrow for the magnitude of the estimate and the precision of the estimate improved, the smaller the upper limit on the prior (Table 3.8).

Additionally, the standard deviation for the mean across all priors with ONeSAMP indicates

Chapter Three –Results

that while the results are somewhat sensitive to the choice of priors, this is to a far lesser

extent than for the wild adult sample (Table 3.8).

For the wild adults, a precise estimate of the inbreeding effective population size

could not be obtained with either analytical approach. The point estimate for LDNe was

-1,130 and the 95% confidence intervals were highly asymmetric, with a lower limit of 389

and an upper limit of infinity (Table 3.8). The mean estimate of NeI obtained from

ONeSAMP ranged from 90-254, with asymmetric 95% confidence intervals ranging from

77-500. The mean estimate of NeI varied greatly depending on the choice of priors and did

not show any trend in the relationship between the value of the estimate and choice of priors

(Table 3.8). This indicates that the precision of the estimates for the wild adults is poor.

NeI of wild adults NbI of cultured juveniles

95% confidence 95% confidence intervals intervals

Estimator Priors Estimate Lower Upper Estimate Lower Upper i) LDNe N/A -1078 389 ∞ 203 106 1087 ii) ONeSAMP 20-10000 185 146 472 173 136 403

20-5000 150 124 305 147 121 283

20-2500 214 162 500 133 110 239

20-1250 254 191 439 - - -

20-500 90 77 108 - - -

Mean 179 ±28 140 ±19 365 ±72 151 ±12 122 ±8 309 ±49 * Where (-) indicates data which were not available with the program ONeSAMP.

Chapter Four – Discussion

4.1 Overview

The present study is the first to investigate the genetic implications of culturing a marine finfish from fertilised wild eggs (ACAAR technique) for use in stock enhancement.

Nine polymorphic microsatellite loci (Table 2.1) were used to compare the genetic compositions of wild adults of Chrysophrys auratus from Cockburn Sound and juveniles of

C. auratus cultured from fertilised eggs, collected from spawning aggregations in Cockburn

Sound (the ACAAR technique). The results of this study suggest that genetic diversity was maintained with the ACAAR technique and that the risk to the wild population following releases, in terms of a reduction in genetic variation, increased inbreeding or a significant reduction to the effective population size, would be very low. These results were consistent with findings from studies of novel techniques for culturing fish for stock enhancement

(Crossman et al., 2011; Gonzalez et al., 2010), yet were generally in contrast to findings from traditional culturing programs (see Araki and Schmid, 2010). This suggests that the

ACAAR technique is superior to traditional methods of culturing for preserving genetic diversity and avoiding the potential negative genetic consequences of stock enhancement.

4.2 Locus Selection

This study investigated the utility of 11, published microsatellite loci (Table 2.1;

Gardner et al., 2014) for comparing the genetic compositions of ACAAR-cultured juveniles and wild adults of C. auratus from Cockburn Sound. Significant deviations from HWE were detected at two of the 11 loci in the wild adult sample (PauA119 and PauD118). These deviations were attributed to a deficit of heterozygotes, as indicated by the positive inbreeding coefficients at these loci (Table 3.3). Such heterozygote deficits commonly result from null alleles, which are alleles that fail to amplify, giving a ‘false homozygote excess’ (Pompanon et al., 2005). Although the results of MICROCHECKER indicated that

69 Chapter Four – Discussion the frequency of null alleles was low (< 0.10) at both loci (Table 3.1), these loci were not used for the juvenile-adult comparisons. This is because null alleles have the potential to bias some of the parameters being compared, such as allele frequencies and genic diversity, and bottleneck analyses (Bonin et al., 2004; Hosking et al., 2004; Pompanon et al., 2005).

There are limited options for dealing with null alleles, and those available are usually expensive and time consuming to implement (see Ewen et al., 2000; Wagner et al., 2006;

Kelly et al., 2011). Hence the decision to exclude these loci from further analyses.

In future studies of Chrysophrys auratus, caution is advised when considering the use of PauA119 and PauD118, because of the above considerations on their deficit of heterozygotes and the presence of null alleles. Evaluation of their performance for the specific population(s) of interest is highly recommended. The remaining nine loci

(PauC102, PauC104, PauC105, PauD104, PauD111, PauD113, PauD116, PauD117 and

PMA1) did not display evidence of null alleles, deviations from Hardy-Weinberg equilibrium or linkage disequilibrium in either the cultured juvenile or adult sample and should be appropriate for use in future studies of C. auratus.

4.3 Reliability of genotype scoring

The strength of the conclusions drawn from this study, regarding the juvenile-adult comparisons is dependent on the reliability of the genotype scoring for the nine loci that were used to make these comparisons (see Appendix 1). There was no evidence for the presence of any scoring errors at these loci. A number of measures were taken to minimize the risk of such errors occurring and/or remaining undetected, as detailed below.

4.3.1 Quality and quantity of DNA

Only extracts yielding high quantity, high molecular weight template DNA were retained for PCR amplification and genotyping. This was important because, the quality and quantity of DNA can be compromised when extracted from tissues sampled via non- invasive methods, as in this study (Ellis et al., 2011; Pompanon et al., 2005). Such extracts

Chapter Four – Discussion are prone to genotyping errors, such as allele dropout (where some alleles at a locus fail to amplify, i.e. null alleles or large allele dropout), false alleles (primer dimers and other PCR artefacts that may be mistaken for PCR product) and may carry an increased risk of contamination (Pompanon et al., 2005; Van Oosterhout et al., 2004).

4.3.2 Contamination with non-target DNA

DNA extractions and PCR amplification with the wild adults and cultured juveniles were conducted separately for each sample, one preceding the other. Equipment was cleaned when moving from large tissue samples (wild adults) to smaller samples (cultured juveniles). Additionally, negative controls were used during both DNA extractions and PCR assays. This was done to avoid/ identify contamination of samples with exogenous DNA or cross-contamination between samples, as this can lead to genotyping errors, namely mistaken alleles, where an assigned genotype is from an individual other than the one being genotyped (Bonin et al., 2004; Pompanon et al., 2005).

4.3.3 Inconsistent PCR amplification

A positive control of high quality and quantity of DNA (muscle tissue) and of known fragment length was included in each plate analysed via capillary electrophoresis, as recommended by Pompanon et al. (2005). GeneMarker software was used to view the raw electropherograms and determine the quality of the PCR products. This was done in order to identify errors resulting from marker characteristics and errors due to differential electrophoretic migration of PCR products (Ewen et al., 2000; Morin et al., 2009). Where

GeneMarker identified that the relative fluorescence units of a particular PCR product were less than 1,000, i.e. where the signal to noise ratio was low and accurate scoring difficult, the sample was re-assayed, in order to maintain consistent and accurate scoring across the dataset.

Chapter Four – Discussion

4.3.4 Fragment length analysis

No errors in allele size calling were detected when the genotypes assigned by the author and an experienced scorer were compared for a random subset of the data. The congruency in assigned genotypes is likely the consequence of the standardization of techniques for scoring each locus prior to analysis, thereby reducing the rate of errors due to differences in size calling. The absence of scoring errors, while only for a subset of 10% of all assigned genotypes, is nonetheless well within acceptable limits of < 2% recommended by Bonin et al. (2004), and gives confidence that the assigned genotypes are likely to be generally reflective of the true genotypes for each fish.

4.4 Genetic diversity per locus

In the present study, the pooled mean allelic richness per locus varied between 3.47 and 21.81, with a mean of 11.88. The expected heterozygosity ranged from 0.158 to 0.913, with a mean of 0.739 (Table 3.3). The mean genic diversity (HE) is consistent with the expected ranges for marine finfish reported in DeWoody and Avise (2000) for 66 loci across 12 species, of 0.77 (±0.19) and falls within ranges reported for both C. auratus

(Gardner et al., 2014; Hauser et al., 2002) and Pagrus major (Gonzalez et al., 2015; Perez-

Enriquez and Taniguchi, 1999).

However, the allelic richness falls below that reported in DeWoody and Avise

(2000) of 19.96 (±6.6). It is also lower than that reported for C. auratus from both Japan and Australia (Perez-Enriquez and Taniguchi, 1999), but comparable to that reported for

Tasman Bay New Zealand, which Hauser et al. (2002) attributed to exploitation over a 50 year period. The moderate levels of polymorphism and high heterozygosity detected for the nine loci employed in this study therefore contain sufficient information content to provide meaningful comparisons of the genetic compositions between the wild adult and cultured juvenile samples.

Chapter Four – Discussion

4.5 Genetic implications for stock enhancement

4.5.1 Genetic diversity

The genic diversity (HE) present in the wild adult sample was preserved in the cultured juveniles, following culturing with the ACAAR technique (mean = 0.739; Table

3.3). Such a neutral effect on heterozygosity is commonly reported following culturing for stock enhancement, even where allelic richness is significantly lower in the cultured progeny (Christie et al., 2012; Karlsson et al., 2008; Lundrigan et al., 2005; Norris et al.,

1999; Obata et al., 2008; Perez-Enriquez et al., 1999). This is because allelic richness is more sensitive than genic diversity to the loss of rare alleles, as low frequency alleles have little impact on heterozygosity (Allendorf, 1986).

The current study also found that the level of genetic diversity present in the wild adult sample (mean= 11.87; Table 3.3) was retained in the cultured juveniles (mean=

11.89). This is in contrast to findings from traditional culturing practices used in stock enhancement, which have been widely reported to have a tendency to reduce genetic diversity in cultured fish (An et al., 2014; Christie et al., 2012; Gardner et al., 2013;

Hamasaki et al., 2010; Karlsson et al., 2008; Kitada et al., 2009; Lind et al., 2012;

Machado-Schiaffino et al., 2007; Nakajima et al., 2014; Norris et al., 1999; Perez-Enriquez and Taniguchi, 1999; Sánchez-Lamadrid, 2002; Sekino et al., 2002; Song et al., 2011;

Wang et al., 2012, 2011). This outcome has generally been attributed to bottlenecks resulting from limited founding broodstocks (An et al., 2014; Jeong et al., 2007; Perez-

Enriquez et al., 1999; Sekino et al., 2003; Wang et al., 2012) and/or low effective numbers of breeders (Horreo et al., 2008; Jeong et al., 2007; Perez-Enriquez et al., 1999; Sekino et al., 2003).

Yet there are culturing programs with which the allelic richness has been unaffected, or largely unaffected, in fish cultured for enhancement. These programs involve the use of very large broodstocks of up to several hundred individuals, e.g. Pacific herring,

Chapter Four – Discussion

Clupea pallasii (Kitada et al., 2009), or the use of novel techniques, such as a single-day egg collection method with black sea bream, Acanthopagrus schlegelii (Gonzalez et al.,

2010) or wild egg and larvae collection with lake sturgeon, Acipenser fulvescens (Crossman et al., 2011). The goal of the ACAAR technique was to utilise a large number of breeders present in Cockburn Sound spawning aggregations, natural spawning mechanisms and the pooling of eggs over multiple spawning events (two, in this case), all of which are predicted to increase genetic diversity in captivity (Gonzalez et al., 2010; Neff et al., 2011) and which likely facilitated the maintenance of the levels of diversity detected in the wild sample, during culturing.

There is evidence that long-term enhancement with genetically depauperate hatchery-reared fish, reared using traditional culturing methods, has the potential to reduce genetic variation in the receiving wild population (Hamasaki et al., 2010; Kitada et al.,

2009). This is of concern because genetic diversity provides the raw material for adaptation to changing environments (Reed and Frankham, 2003) and populations possessing little genetic diversity are likely to have a lower evolutionary potential and a lesser chance of long-term persistence (Fisher, 1958; Markert et al., 2010; Reed and Frankham, 2003).

However, where stocking is less intensive (Gonzalez et al., 2013) or where hatchery techniques maintain diversity in the cultured fish (Kitada et al., 2009; Nakajima et al., 2014;

Sugaya et al., 2008), reductions in the genetic diversity of the receiving population following enhancement have been avoided. This suggests it is unlikely that releases of cultured juveniles of C. auratus, with very similar levels of genetic diversity to their wild counterparts, will have an appreciable negative impact on the genetic diversity of the wild population.

It must be noted though, that retention of genetic diversity at neutral microsatellite markers does not necessarily indicate that diversity at quantitative loci, of evolutionary importance, is preserved to a similar extent (Bouzat, 2010; Larsen et al., 2007).

Chapter Four – Discussion

Nevertheless, in the absence of practicable means to investigate loci underlying quantitative traits in this study (see Naish and Hard 2008), the assumption that diversity at neutral loci can be indicative of genome-wide variation provides a means of interpreting the significance of these results from neutral loci (Kirk and Freeland, 2011; Vilas et al., 2015).

Therefore, while not a direct relationship, the likelihood of maintaining neutral genetic diversity in the wild population following releases of ACAAR-cultured fish, may confer advantages for the long-term persistence of the wild population.

4.5.2 Allelic composition

Allelic richness was effectively the same in the cultured juvenile and wild adult samples, as most of the common alleles (of frequencies > 0.05) present in the wild adults were transferred to the cultured juveniles. However, despite the retention of common alleles in the cultured sample, significant differentiation between the allelic compositions of the cultured juveniles and wild adults was detected. This was owing to the presence of several rare alleles that were unique to each sample: 14 in the cultured juveniles and 15 in the wild adults. Insufficient sample sizes may have contributed to the presence of private alleles in each sample. A greater number of unique alleles were detected at loci with higher levels of polymorphism (Table 3.5). Rarefaction analysis indicated that only the two most polymorphic loci PauD117 and PauD113 may have been insufficiently sampled to detect all alleles present (Figure 3.2). Yet even at loci with lower levels of polymorphism, unique alleles were still detected in each sample, indicating the sample sizes (n = 54; 58) may have been insufficient at these loci also (Table 3.5).

The 14 alleles detected in the cultured juveniles, but absent in the sample of wild adults indicates sampling bias. Clearly, because confidence in the assigned genotypes is high (see section 4.2), these 14 alleles must have been present in wild adults of C. auratus spawning in Cockburn Sound, to have appeared in their offspring. Therefore, it is likely that the sample of wild adults collected in this study was not entirely representative of all the

Chapter Four – Discussion adult C. auratus spawning in Cockburn Sound. The absence of 15 wild adult alleles in the cultured juvenile sample requires a slightly different explanation. Under traditional culturing techniques for stock enhancement, it is common for rare alleles present in wild populations to be absent in cultured offspring. This is typically attributed to founder effects from broodstock selection and associated bottlenecks (Alarcón et al., 2004; Blanchet et al.,

2008; Gardner et al., 2013; Gonzalez et al., 2013; Kitada et al., 2009; Nakajima et al., 2014;

Sekino et al., 2002). Yet the ACAAR technique does not employ broodstock for culturing.

Therefore, the absence of the 15 wild adult alleles in the cultured juvenile sample in the present study is likely the result of a bottleneck following the sampling of eggs and culturing of the juveniles, i.e. the combination of sampling bias and mortality.

The wild eggs, from which the juveniles in this study were derived, were collected over only two nights of sampling and would represent only a fraction of fertilised eggs produced by spawning aggregations of Cockburn Sound snapper. Survival between hatching and release (and sampling for genetic analysis) was 5.9% and 3.2% for the respective November 10 and 17, juvenile cohorts (ACAAR, 2015). Further, only approximately 120 juveniles were sampled from the 2,500 cultured for release (ACAAR,

2015) and of these, only 54 were sampled for genetic analyses. Therefore, it is possible that the 15 alleles absent in the sample of 54 fish genotyped were in fact present in the larger cohort of juveniles cultured with this technique, but simply missing in the small sample genotyped. It is also likely that the combination of sampling bias, introduced at each stage of the sampling and culturing process, resulted in a modest bottleneck. This is supported by the heterozygote excess detected in the cultured juveniles (Table 3.3), which occurs as heterozygosity is less sensitive to losses of rare alleles (Allendorf, 1986).

Yet the bottleneck analysis failed to detect any evidence that the cultured juvenile sample had experienced a recent bottleneck. This is a finding which is common to studies of the genetic implications of culturing, which employ empirical means of testing for

Chapter Four – Discussion bottlenecks (Bouza et al., 2002; Exadactylos et al., 2013; Wang et al., 2010, 2011, 2012), despite a significant reduction in allelic richness in cultured progeny being observed in some of these studies (Wang et al., 2011, 2012). This suggests that bottleneck analyses, while effective in detecting bottlenecks in certain situations (see Cornuet and Luikart, 1996;

Cristescu et al., 2010), are perhaps not very sensitive to the changes that occur during culturing.

Despite the likelihood that the cultured juveniles underwent a modest bottleneck during culturing with the ACAAR technique, in general, the allelic compositions of the cultured juveniles were similar to those of the wild adults. However, the absence of 15 of the wild adult alleles in the cultured juvenile sample could potentially reduce their frequency in the combined wild population following large-scale releases, if the cultured fish were to breed successfully. Such changes in the allelic compositions/frequencies of wild populations, following the release of cultured fish, has been widely reported in salmonids (Jasper et al., 2013; Machordom et al., 1999; Sušnik et al., 2004). This effect is of concern because rare alleles are more susceptible to loss via genetic drift (Waples, 1990) and the loss of any allele is an irreversible process, which may have significant evolutionary consequences (Vuorinen, 1984).

However, because the 15 alleles were present (albeit in low frequencies) in the wild population, it is unlikely that even large-scale releases would be capable of ‘swamping’ the wild population and reducing their frequency to a point where they would be more likely to be exposed to loss via drift than they currently are (Tringali and Bert, 1998; Waples, 1990).

Furthermore, the probability of the loss of alleles is largely governed by the effective population size of the combined population (discussed below) and, where Ne is large, there may be little risk of losing rare alleles (Tringali and Bert, 1998).

Chapter Four – Discussion

4.5.3 Inbreeding and relatedness

In the current study, mean heterozygosity was high in both samples (0.739), and the inbreeding coefficient, FIS (the probability that two alleles are identical by descent in a particular individual) was negative in the cultured juveniles (-0.019; Table 3.3). With traditional culturing techniques, neutral effects on the rate of inbreeding (An et al., 2014;

Borrell et al., 2007; Christie et al., 2012; Wang et al., 2011) or positive inbreeding coefficients are the most common outcome (Gonzalez et al., 2008; Jeong et al., 2007;

Kitada et al., 2009) and the rates of inbreeding can be as high as 5.6% in cultured fish

(Jeong et al., 2007). Negative inbreeding coefficients are rare, but not unheard of in studies of traditional culturing (Lind et al., 2009; Norris et al., 1999) and have been attributed to bottlenecks associated with significant reductions in allelic richness. In the present study, the negative inbreeding coefficient can be attributed to a modest bottleneck and the low levels of relatedness between the cultured fish (see below). Responsible culturing guidelines suggest a loss of heterozygosity of 1% per generation due to inbreeding, i.e. a positive FIS of 0.010, is acceptable during culturing (FAO, 2008; Fraser, 2008). The mean inbreeding coefficient for the ACAAR-cultured juveniles is well below this limit, indicating that heterozygosity would be unlikely to decrease due to inbreeding in successive generations.

Mean relatedness between pairs of individuals is another method of inferring the rate of inbreeding between individuals in a sample, with lower mean relatedness being associated with lower rates of inbreeding (Ritland, 1996). The mean relatedness between pairs of fish in both the cultured juvenile and wild adult samples was low (less than first cousins, i.e. unrelated) and similar between the two samples. Likewise, a high proportion of pairs in both samples were assigned to an unrelated relationship (88.3%) and confidence in these unrelated assignments is high, based on the mean posterior probabilities of assignment to an unrelated relationship (Figure 3.5; Table 3.7). Together, these results suggest that levels of inbreeding were low in both samples and not demonstrably greater in the cultured juveniles. Similar to these results, Crossman et al. (2011) found a lower mean relatedness

Chapter Four – Discussion and co-ancestry in eggs and larvae spawned naturally in the wild, than progeny cultured using traditional methods. Indeed, mean relatedness tends to be greater in traditionally cultured progeny compared to wild populations (Blonk et al., 2009; Christie et al., 2012;

Lind et al., 2009). However, this is usually attributed to low effective numbers of breeders and the ACAAR method, and study by Crossman et al. (2011) aimed to utilise a greater number of effective breeders than traditional methods (see below).

The ACAAR technique ensures that broodstock are not needed for the culturing of

C. auratus for stock enhancement. Therefore, there is no concern for the potential effects of inbreeding between fish retained in the hatchery for successive generations for use as broodstock. Nevertheless, there would be a risk of inbreeding depression in the wild population if the cultured juveniles displayed evidence of inbreeding, which has implications for the evolutionary potential of the wild population (Willi et al., 2006).

Inbreeding makes individuals more homozygous, exposing deleterious recessive genes to selection or eliminating the increased fitness arising from heterozygosity (Wang et al.,

2002). Yet there is little evidence from long-term studies, of inbreeding depression occurring in wild populations as a consequence of releases of cultured fish (Araki and

Schmid, 2010). Therefore, the high heterozygosity, negative inbreeding coefficient and low mean relatedness, in the cultured juvenile sample, and the similarity of these values to those in the wild adults, gives confidence that releases of these fish are unlikely to lead to an increase in the rate of inbreeding in the wild population, or increase the risk of inbreeding depression.

4.5.4 Effective population size

It was not possible to provide a meaningful comparison between the estimated one- sample (i.e. inbreeding) effective population sizes of the cultured juveniles and that of the wild adults. This is because the juvenile sample consisted of fish from a single cohort, whereas the adult sample comprised multiple cohorts and the relative contributions of these

Chapter Four – Discussion cohorts to the sample were unknown. The difference in the composition of the samples has critical implications for interpreting the estimates of effective population size (Waples,

2005). In the case of the juveniles, the inbreeding effective number of breeders in one reproductive cycle, NbI, was estimated. This relates to the expected decrease in heterozygosity due to inbreeding in a single cohort (Hare et al., 2011; Waples, 2005;

Waples et al., 2014). In the case of the wild adults, the inbreeding effective population size per generation (NeI) was estimated. This predicts the decrease in heterozygosity associated with inbreeding over successive past generations (Crow and Denniston, 1988; Hare et al.,

2011; Luikart et al., 2010), where all cohorts in a generation of an iteroparous species

(having overlapping generations) are sampled evenly (Waples, 2005; Waples et al., 2014).

While predictions about the relationship between effective population size per generation and effective number of breeders per reproductive cycle are emerging for many species, including fish (Serbezov et al., 2012; Waples et al., 2014), the ratio of NbI to NeI can vary up to six-fold between taxa (Waples et al., 2013). Therefore, these parameters cannot be directly compared in this study and will be treated separately in the following discussion.

Furthermore, in future studies of the ACAAR technique it is recommended that wild juveniles of the same cohort as the cultured juveniles be sampled, to provide a direct comparison of estimates of NbI.

4.5.4.1 Reliability of estimates

The current study used two linkage disequilibrium methods to estimate the effective population size of the wild adult (NeI) and cultured juvenile (NbI) samples. For the cultured juveniles, the two methods returned generally congruent results. The estimates obtained by

ONeSAMP, considered the superior estimator (Luikart et al., 2010), were more precise than those for LDNe, with relatively narrow confidence intervals, given the size of the estimate, and small standard errors for the mean estimate across all runs. Given the increasing precision of the ONeSAMP estimate for runs with smaller upper limits on the prior, and

Chapter Four – Discussion given the runs with the smallest prior intervals were unable to be completed, these results would benefit from the addition of data from these missing runs.

The estimates of NbI for the cultured juveniles from LDNe (203, confidence intervals

= 106-1,087) and ONeSAMP (133-173, confidence intervals = 110-403) were as much as an order of magnitude (x 10) greater than values published for species cultured for stock enhancement using traditional methods (Christie et al., 2012; Gold et al., 2008; Lind et al.,

2009; Perez-Enriquez et al., 1999). Although, similar to the current study, Crossman et al.

(2011) found that the estimated effective number of breeders for wild-caught eggs (and to a greater extent wild larvae) was an order of magnitude greater than the estimate for traditionally cultured A. fulvescens. Their estimates of effective number of breeders were as large as 147 (confidence intervals = 103–224) in the wild larvae, which is consistent with the results of the current study.

Conversely, for the wild adults, a reliable estimate of NeI could not be obtained with either analytical approach. The LDNe estimate (-1,130, confidence intervals = 389-∞) demonstrated evidence of sampling bias and the ONeSAMP estimates (90-254, confidence intervals = 77-500) were sensitive to the choice of priors. The LDNe estimator implements a correction for correlation between alleles due to sampling bias and where the signal from sampling error is greater than that from linkage disequilibrium, negative values can arise

(Waples and Do, 2010). This evidence of sampling error is related to a seldom-questioned assumption of single-sample estimators: that in the target species, generations are discrete

(Waples et al., 2014). For an iteroparous species such as C. auratus, with overlapping generations, this assumption is clearly violated. In species with overlapping generations, age structuring generates high covariance of allele frequencies between cohorts (Serbezov et al., 2012). All cohorts must therefore be sampled evenly across an entire generation to obtain an accurate estimate of NeI (Hare et al., 2011; Waples et al., 2014). Preference of any

Chapter Four – Discussion particular cohort causes the estimate of effective population size to be somewhere between

NeI and NbI (Waples, 2005), thereby downwardly biasing estimates (Hare et al., 2011).

Chrysophrys auratus are long-lived, with infrequent, strong year classes (Wakefield,

2006) making equal sampling of cohorts difficult. Additionally, only a narrow range of size classes were sampled in this study (mean = 746 ±11 mm). Size at maturity (L50) for Cockburn

Sound snapper is 585 mm for females and 566 mm for males and the maximum recorded length is 1056 mm (Wakefield 2006), suggesting that in this study, sampling was biased towards larger individuals. Evidence for this source of bias is strengthened by the fact that greater precision was obtained for estimates where a single cohort was sampled, with the cultured juveniles, compared to the wild adults. Therefore, confidence in the estimate of effective population size with the wild adults is low and this estimate will not be discussed further, while the estimate of NbI for the cultured juvenile sample is considered more appropriate for further interpretation.

4.5.4.2 Implications of effective number of breeders

The estimate of the inbreeding effective number of breeders for the cultured juveniles (133-203), provides a means of assessing whether the number of effective breeders utilised with the ACAAR technique was sufficient to avoid inbreeding in subsequent generations. A widely published rule of thumb for minimum effective sizes required to maintain heterozygosity (and prevent inbreeding) is 50 and the minimum to maintain evolutionary potential of a population is 500 (Franklin and Frankham, 1998;

Palstra and Ruzzante, 2008). While such hard-and-fast guidelines are unlikely to be meaningful in all situations, they provide a means of evaluating the estimates obtained in this study. Under these guidelines, the estimate for the cultured juveniles appears to be of a reasonable size to prevent a significant increase in homozygosity as a result of inbreeding.

This is supported by the high observed heterozygosity, negative inbreeding coefficient and low mean relatedness in the culture juvenile sample.

Chapter Four – Discussion

However, the effective number of breeders contributing to the cultured juveniles may not be sufficient to maintain evolutionary potential. Following the recommendation of

Gonzalez et al. (2010), it is suggested that, in future, the ACAAR technique sample eggs from a greater number of spawning nights over the peak spawning period (as opposed to only two for the cultured juveniles in this study), in order to maximize the number of effective breeders contributing to the cultured offspring.

The estimate of effective population size can also inform about the probability of negative impacts to the effective population size of wild populations, upon release of cultured juveniles, i.e. a Ryman-Lairke effect (Ryman and Laikre, 1991). In order to accurately assess the probability of a Ryman-Lairke effect upon release of ACAAR- cultured juveniles, the Ne of the wild population must be known (along with other parameters, see section 2.2.2 for equation). Unfortunately, it was not possible to obtain a good estimate of effective population size for the wild population in this study.

Nevertheless, studies modelling the Ryman-Lairke effect indicate that if the goal is to maintain the effective population (Net) > 500 for the combined population, where the cultured juvenile effective size (NeC) is > 50, the cultured juvenile contribution (x) must be

< 17%. Where NeC is > 100, the cultured contribution must not exceed 30% to achieve the same goal (Ryman and Laikre, 1991; Tringali and Bert, 1998). Given the estimated cultured juvenile NbI of 133-203, and considering the Ne for this sample is likely to be greater

(Waples et al., 2014), this suggests that large contributions of the ACAAR cultured juveniles may be possible without compromising the evolutionary potential (reducing Ne <

500) for the combined population. However, more accurate estimates for both the wild adult and cultured juvenile samples are required to test this prediction (see section 4.7).

Lastly, it is also important to consider the possibility that the release of cultured juveniles into Cockburn Sound may overcome any recruitment limitation and thereby increase the abundance of C. auratus. If this were to occur, a second scenario can be

Chapter Four – Discussion considered, one which is seldom mentioned in the literature—the effect of enhancement on the effective population size of the wild population over multiple generations (cf. the

Ryman-Laikre effect, which only considers a single generation). Where enhancement can increase the census size of the wild population, within very few generations the variance in

Ne (which is large in the first few generations due to the large proportional contribution of the cultured fish) decreases, and the effective population size will actually increase with successive generations. Furthermore, where variance in family-size is large (which is likely the case with Cockburn Sound snapper due to their broadcast spawning), enhancement can result in an immediate increase in effective population size from the first generation of enhancement (Wang and Ryman, 2001). Therefore, while there is legitimate concern for the reduction in the combined Net via a Ryman-Laikre effect, there is also the potential for the release of juveniles cultured under the ACAAR technique to actually increase the Ne of the receiving population. Nevertheless, Wang and Ryman (2001) stress the theoretical nature of these predictions and the need to evalutate changes in Ne for each particular enhancement program and over the life of the program.

4.6 Broader applications

The results discussed in the previous sections indicate that the ACAAR technique has the potential to retain genetic diversity, avoid inbreeding and retain effective population sizes at appropriate levels in the wild, following releases of cultured juveniles of C. auratus.

The moderate levels of polymorphism detected in C. auratus from Cockburn Sound suggests that this positive outcome was not simply the consequence of low levels of standing genetic diversity, easily captured in the wild, cf. Pacific threadfin, Polydactylus sexfilis (Pan and Yang, 2010). Therefore, these results may be broadly applicable to other species. Indeed, the moderate levels of polymorphism detected in C. auratus are of a similar magnitude (although allelic richness was slightly lower) to other marine finfish (DeWoody and Avise, 2000). While it may be more difficult to capture the standing genetic diversity in

Chapter Four – Discussion species with higher levels of polymorphism, nonetheless these results from the moderately polymorphic C. auratus, suggest that the ACAAR technique may be employed with other aggregate spawning marine finfish to a similar effect.

4.7 Further research

Several recommendations to improve the methods detailed in this study have been suggested throughout this discussion and it is worth reiterating and expanding on these, to provide a resource for future research.

4.7.1 Recommendations for culturing

4.7.1.1 Pooling eggs

The proportion of rare alleles absent in the cultured juvenile sample could be reduced in the future by increasing the number of sampling occasions for C. auratus eggs, and pooling the collected eggs. Pooling eggs from multiple spawning events has been shown to provide a more representative sample of the genetic composition of wild populations than traditional methods of culturing (Gonzalez et al., 2010; Nugrohoa and

Taniguchi, 2004). While beyond the scope of this thesis, a sample of C. auratus eggs, collected by the ACAAR as part of this project, is available to assist in assessing how much genetic variation is captured in a single sampling event. This sample will be assessed as part of the broader Recreational Fishing Initiatives Fund (RFIF) project to assess the genetic implications of the ACAAR technique.

4.7.2 Limitations and recommendations for genetic analyses

4.7.2.1 Detection of genetic variability

Rarefaction analysis indicates that sample sizes (K) at most loci were sufficient to detect most of the alleles present in both samples. However, some rare alleles were not detected in the cultured juveniles and wild adults, indicating the sample sizes may not have been sufficiently large to capture all the genetic variation present in the source

Chapter Four – Discussion population(s). There are no rules of thumb for minimum sample sizes that hold true for all situations (Gapare et al., 2007; Hale et al., 2012; Kalinowski, 2005). For example, where the goal is to capture ≥ 95% of alleles with frequencies > 0.05 Hale et al. (2012) suggest samples of 25-30 individuals are sufficient. However, studies of the effects of culturing for stock enhancement are concerned with the loss of rare alleles and so detecting alleles of frequencies < 0.05 is critical. The only accurate method to determine the appropriate sample size for C. auratus in Cockburn Sound would be to sample a very large number of individuals and use rarefaction methods to determine at what point the additional detection of alleles becomes too costly, or time consuming, to justify. The implementation of this recommendation may not be feasible given the diminishing returns in terms of alleles detected with increased sample sizes (Kalinowski, 2005) and would depend on time and budgetary constraints.

4.7.2.2 Estimates of effective population size (NeI)

4.7.2.1.2 Number of loci sampled

While the estimates obtained for the cultured juveniles were of acceptable precision, the precision of both the cultured juvenile and wild adult estimates could be improved by assaying a greater number of loci (L). This is because the precision of linkage disequilibrium estimates of Ne increases by the square of the number of loci used (Luikart et al., 2010). However, Waples and Do (2010) found that doubling the sample size (K) or the number of alleles per locus (A) results in similar increases in precision to doubling the number of loci. Given the additional effort to detect and optimise PCR conditions for new microsatellite loci (see Gardner et al. 2014), increasing the number of loci is unlikely to provide a greater benefit compared to increasing the number of individuals sampled, which would have additional benefits such as detecting additional rare alleles.

Chapter Four – Discussion

4.7.2.1.3 Precise effective population (NeI) estimates

Improvements to the methods for sampling adult Cockburn Sound snapper would likely improve the precision of the NeI estimate. Sampling evenly across cohorts would provide a means of achieving this. Additionally, collecting age and sex-specific life history information for each cohort would enable the use of a more sophisticated Ne estimator, capable of accounting for the effects of overlapping generations, such as the program

AgeNe (Waples et al., 2011). This would likely produce a more accurate estimate of Ne, and would then enable prediction of the likelihood of a Ryman-Laikre effect.

4.7.2.1.4 Comparable effective breeder (NbI) estimates

Collecting samples of wild juveniles from the same cohort as the cultured juveniles would provide two comparable estimates of NbI. This would give an indication of the proportion of effective breeders sampled with the ACAAR technique (cultured juveniles), compared to those available in Cockburn Sound over the entire spawning season (wild juveniles). While genetic analysis of a wild juvenile sample is beyond the scope of this thesis, these samples have been collected by the DoFWA and will be assessed as part of the broader RFIF project.

4.7.2.3 Impacts on other areas

The current study did not consider the potential effect of stock enhancement with

ACAAR cultured juveniles on C. auratus beyond Cockburn Sound. Kitada et al. (2009) suggest that the effects of stock enhancement on wild populations will be localised to the area of release, i.e. Cockburn Sound. Yet the extent of potential genetic effects of releases of ACAAR-cultured C. auratus into Cockburn Sound would depend on the levels of demographic connectivity and gene flow between populations along the WA coast and very little is currently known about these dynamics (Fairclough et al., 2013; Lenanton et al.,

2009). The interpretation of the results presented in this study, and those of the broader

Chapter Four – Discussion

RFIF project, would therefore benefit from a greater understanding of these dynamics, which is currently under investigation by Gardner (Murdoch University, unpublished data).

4.6 Final remarks

This thesis represents a preliminary investigation into the genetic implications of a technique for culturing an aggregate-spawning marine finfish, from wild-caught eggs, for stock enhancement. The findings reported here suggest that, overall; this technique maintained the genetic variation (allelic richness and heterozygosity) present in the wild population of Chrysophrys auratus in Cockburn Sound, Western Australia. The genetic compositions of the juvenile fish cultured under this technique were very similar to that of the wild adults sampled. While some rare alleles were missing in the cultured juveniles, which may increase the risk of their loss via drift in the combined population, the implementation of some simple recommendations such as pooling eggs collected from multiple spawning events, would likely reduce this risk. The high level of heterozygosity, low mean relatedness and large number of effective breeders contributing to the cultured juvenile sample suggests that these fish pose a limited risk of increasing the level of inbreeding in the wild population, or significantly reducing the combined effective population size over the long-term. However, the implementation of the recommendations in section 4.5, would improve the strength of these final conclusions.

As a whole, the findings of this study suggest that the ACAAR technique for culturing C. auratus for stock enhancement is likely to have positive genetic implications, compared to traditional methods of culturing for enhancement, and its use is likely to pose little risk to the genetic integrity of the wild population of C. auratus in Cockburn Sound.

Additionally, these results may be applicable to other species, such as P. major. Therefore, it may be possible to dispense with the use of broodstock with other aggregate spawning marine finfish, by collecting wild eggs for culturing and use in stock enhancement.

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Appendix – Raw genotype data

Appendix 1 – Raw genotype data

PauD113 PauD117 PauD104 PauC102 PauC104 PauC105 PauD111 PauD116 PMA1 POP FC007 , 223255 226254 267283 324336 243243 208208 340348 182214 129131 FC008 , 219243 218246 275283 320320 243247 204204 348364 206206 129135 FC009 , 239263 238246 271275 332332 243243 204208 344356 198214 131131 FC010 , 227247 218226 267283 316324 243247 204208 340348 210210 131131 FC011 , 215263 218230 255267 332336 243243 204204 336356 202206 131133 FC012 , 231231 222254 279279 328332 243247 204208 340348 198210 131133 FC013 , 227243 218230 263275 320336 243243 204208 348376 194210 131131 FC014 , 227247 209266 263307 324324 243243 208208 348352 194214 129133 FC015 , 215235 234254 263279 328336 243243 204208 356360 198206 131133 FC016 , 227243 218250 275275 324332 243243 204208 356364 202218 131131 FC017 , 235243 201222 287291 320352 243243 204208 332356 198206 133133 FC018 , 231275 234238 259259 324327 243247 204208 324344 174202 131131 FC019 , 223247 238242 267271 328328 243243 204212 336344 198210 131133 FC020 , 231235 190246 275283 336348 231243 204204 336340 194194 133133 FC021 , 235263 218246 275283 328352 243243 204208 332344 182214 133133 FC022 , 203223 222230 283295 324327 243243 204204 336348 182194 131133 FC023 , 231231 234242 247263 320328 243243 208208 352376 198206 129129 FC024 , 207255 226226 267275 331336 243243 204208 332348 214226 129133 FC025 , 223231 246258 275283 328352 243243 204204 356356 182210 129131 FC026 , 219243 213230 275279 320328 243243 204208 340364 194210 131133 FC027 , 235255 201209 279279 327328 243243 204208 352360 190194 131131 FC028 , 243255 238254 255267 320352 243243 208212 328352 198206 131133 FC029 , 235247 226254 271279 324328 243243 192208 324336 198202 131137 FC030 , 235255 226246 271283 324324 243243 208208 336368 198206 131133 FC031 , 227243 226230 283283 320328 243243 204208 352352 186202 129131 FC032 , 211267 226234 275283 320328 243243 208208 340356 206210 131137 FC033 , 219231 218226 255271 324328 243243 204208 344364 194198 131133 FC034 , 215255 218226 267279 320328 231243 204212 348364 198218 131133 FC035 , 235251 234246 267295 320320 243243 208212 332340 166206 131131 FC036 , 239255 214218 283307 320328 243243 204204 340356 194206 131131 FC037 , 219231 226246 267299 336348 243243 208208 328356 190214 133133 FC038 , 243247 238242 259271 324332 243243 204208 324364 194202 131131 FC039 , 223243 230254 255267 320328 243243 208212 352368 194206 131131 FC040 , 243247 242246 279299 320324 243243 208208 340352 206214 129133 FC041 , 219243 214238 267275 312336 243243 208208 352364 174206 131131 FC042 , 223231 214226 247271 324324 243247 208208 340348 202202 131131 FC043 , 207227 234246 247259 320332 243243 204208 344344 194206 129133 FC044 , 223239 242242 275283 331336 243243 204208 352352 210214 129131

109

Appendix – Raw genotype data

FC045 , 215223 226226 263267 320328 243243 204208 352376 198218 131131 FC046 , 231235 194230 279295 327332 231243 204208 328368 190210 131131 FC047 , 215215 226238 291295 324336 243243 204208 348364 186206 131131 FC048 , 215219 226226 247279 332344 243243 208208 332364 198210 131133 FC049 , 247263 213222 283295 324328 243243 204208 328356 202214 131131 FC050 , 215223 238242 267271 320336 243243 204208 344344 190198 129131 FC051 , 239247 210218 247255 320336 243243 208212 336340 210214 129131 FC052 , 235287 230242 271283 316336 243243 208208 336344 206210 131131 FC053 , 207227 222242 279283 331336 231247 208208 336344 198206 131131 FC054 , 211243 234262 271279 324328 243243 204208 344360 202206 131133 FC055 , 235275 218226 279283 320328 243243 208208 340352 182206 131131 FC056 , 227263 226226 267275 320340 243243 204208 348360 182210 131135 FC057 , 219243 250274 263267 320332 243243 208208 348364 202202 131131 FC058 , 203243 217250 283299 332336 243243 204208 348364 182182 131131 FC059 , 235239 238238 267267 324332 243243 204208 340348 186206 129131 FC060 , 223259 230238 271295 320336 243243 208212 348352 186206 129133 FC061 , 231243 206258 275279 320331 243243 208208 352352 202214 131131 FC062 , 219231 217254 275295 324331 243243 204204 340348 202226 133137 FC063 , 227243 210246 267275 320336 235243 208208 344352 210214 131131 FC064 , 243247 218262 255279 328332 243243 208208 332344 182194 129131 POP CJ001 , 215235 226234 259287 316320 243243 208208 320372 198222 131135 CJ002 , 235255 218226 279295 328352 243243 208208 352352 182202 129131 CJ003 , 231243 213218 255283 324328 243243 204208 324356 202210 129131 CJ004 , 247287 218238 275283 331348 243243 204208 340356 198214 129131 CJ005 , 243255 201234 271287 332348 243243 208208 336364 194222 135135 CJ006 , 227263 230262 263275 332336 243243 204204 360368 182198 131133 CJ007 , 227243 221258 255267 316324 243243 204208 340352 182194 129135 CJ008 , 219223 226230 255271 320324 243243 208208 344364 194206 131133 CJ009 , 219227 230246 247283 320324 243243 208208 340340 182218 133139 CJ010 , 243247 226234 267279 331331 243243 204208 344348 182194 131131 CJ011 , 215263 217226 275295 328336 243243 204204 340348 190198 131133 CJ012 , 243251 214242 255299 324332 243243 204208 328352 202222 131135 CJ013 , 231247 230246 259263 336340 243243 208208 328344 182202 131131 CJ014 , 231239 225230 247259 320324 243243 208208 344364 222226 129133 CJ015 , 219223 218234 283291 336340 243247 200208 332360 202206 131131 CJ016 , 223239 226238 275283 328340 243243 208208 308356 190242 131131 CJ017 , 239259 230238 247247 320324 243243 208208 332344 194206 131131 CJ018 , 255259 210221 263271 332336 243243 204208 328340 210214 129131 CJ019 , 235239 218226 247283 320328 243243 204208 340356 198218 129133 CJ020 , 227243 210242 271299 324328 243243 204204 328352 194202 131135 CJ021 , 223255 226230 263283 328336 243243 208208 340364 194218 131133 CJ022 , 227239 238246 255295 320328 243243 204212 348348 202202 133133 CJ023 , 227235 246302 247295 316328 243243 208208 344348 202202 131133 CJ024 , 255259 190246 259291 320328 243247 204208 344352 166194 131133 CJ026 , 211223 226226 271283 320336 243243 204208 324340 182206 129131 CJ027 , 203235 214254 267267 316324 243243 208208 352368 202210 131131 CJ028 , 227231 226226 263267 320340 243243 208208 332360 202202 129131 CJ029 , 235239 226230 247267 320328 231243 204208 316316 190198 131131 CJ030 , 235251 226234 279283 324348 243243 204208 348352 182190 129131 CJ031 , 223251 238242 263283 320331 243243 204208 352372 206222 131139 CJ032 , 215247 217226 267283 332336 243243 204208 344368 174202 131133 CJ033 , 215239 230250 271275 327328 243243 204208 344348 194202 131131

110

Appendix – Raw genotype data

CJ034 , 239243 218218 263283 328331 243243 208208 360364 202218 131133 CJ035 , 239247 230262 247279 348352 231243 208208 332356 190202 131133 CJ036 , 235255 201226 271271 324328 243243 208208 356368 186198 133133 CJ037 , 211223 221225 275287 316328 243243 208208 336352 202218 131133 CJ038 , 227259 234242 271279 320360 243243 208212 344344 198198 131131 CJ039 , 243247 210217 263287 320331 243243 208208 340348 190190 131131 CJ040 , 223235 218246 267267 320336 243247 204208 336356 186198 129133 CJ041 , 239243 254262 283291 320328 243247 204208 332352 186242 131133 CJ042 , 239239 226230 247279 320331 243243 204208 360364 182206 131133 CJ043 , 235239 226238 247271 316328 243247 208208 344348 194206 131135 CJ044 , 223227 210238 283287 320331 243243 208208 348348 194226 129131 CJ045 , 211227 218254 275275 316328 243243 204208 336344 170198 131133 CJ046 , 227251 218238 259279 320336 243243 204212 328348 186214 131131 CJ048 , 223255 218230 263303 324331 243247 204208 332344 198202 131131 CJ049 , 243243 226254 247295 324328 243243 208208 344356 182206 131131 CJ050 , 223247 222230 287299 320331 243243 204208 332344 194202 129133 CJ051 , 239239 230238 247283 324328 243243 208208 332344 194206 131131 CJ052 , 219235 222230 247291 324331 243243 208212 352356 202214 129129 CJ053 , 235243 234242 267287 324324 243243 208212 336348 166222 129135 CJ054 , 243247 218218 275287 332348 243243 208208 348356 166226 129129 CJ055 , 239239 226246 267283 316320 243243 204208 360372 206214 131131 CJ056 , 259263 226250 255271 328332 243243 204208 356364 206214 131131

111