shengjieren · geneticimprovementofthepacificwhite shrimp(penaeusvannamei)inchina shengjieren...

GENETIC IMPROVEMENT OF THE PACIFICWHITESHRIMP (PENAEUS VANNAMEI) IN CHINA

Shengjie Ren

M.Sc. (Hydrobiology)

B.Sc. (Aquaculture)

Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy

School of Biological and Environmental Sciences

Science and Engineering Faculty

Queensland University of Technology

2020

Genetic Improvement of the Pacific White Shrimp (Penaeus vannamei) in China i

Keywords

Aquaculture, quantitative genetics, population genetics, microsatellite markers,

genetic diversity, population structure, prawn aquaculture, Bayesian assignment,

Penaeus vannamei, strain performance, genetic variation, heritability, genetic

parameters, body weight, reproductive performance, broodstock, multiple spawning,

genetic correlations, phenotype correlations, reproductive traits

ii Genetic Improvement of the Pacific White Shrimp (Penaeus vannamei) in China

Abstract

Pacific white shrimp (Penaeus vannamei) is currently in trade value terms, the

most important food commodity in global aquaculture. Long term sustainability of

farmed marine shrimp is crucial, not only because of its socioeconomic importance,

but also because this product makes a significant contribution to world food security.

Results of the current project will assist the design of future breeding programs for

marine shrimp that seek to develop locally adapted strains that target specific

farming and market conditions in China.

The first step in the current project applied seven microsatellite markers to assess

genetic resources for Pacific white shrimp that were available in China by

documenting relative levels of genetic variation, extent of stock differentiation and

genetic relatedness as an initial step towards producing a base population for a long-

term family-selection breeding program. Based on the genotypic information that

identified 4 distinct groups, a complete 4 × 4 diallel cross was conducted to develop

a base population for the family selection breeding program. Quantitative genetic

analysis of growth traits in the base population confirmed that a substantial

component of additive genetic variance (BW1: h2 = 0.52 ± 0.09; BW2: h2 = 0.44 ±

0.07) was available that could be used to improve relative stock productivity.

The next component of the project focused on optimising reproductive

performance of females in the base population by trialling two different rearing

conditions for broodstock: recirculating tanks (RT) vs earthen ponds (EP). Results of

the trial indicated that no significant differences were present for the majority of

reproductive performance traits examined between broodstock reared in RT vs EP

Genetic Improvement of the Pacific White Shrimp (Penaeus vannamei) in China iii

environments. Females exposed to the EP treatment however, were observed to show

significantly higher mean spawning frequency than their counterparts in the RT

treatment. No evidence was detected however, for reproductive exhaustion in

females that spawned multiple times vs those that spawned only once.

The final study undertook a quantitative genetic analysis of heritability for

reproductive traits in the base population and results showed that there was potential

to improve a number of key reproductive traits in mature females (notably, number

of eggs per spawn (NE), number of nauplii per spawn (NN), and spawn frequency

(SF)) via genetic selection, but that egg hatching rate per spawn (HR) and number of

eggs produced relative to individual female weight (FE) traits, were unlikely to be

improved via this approach. Results of genetic correlations between body weight at

spawning and reproductive traits also provided no evidence to suggest that improving

mean body weight will produce potentially negative effects on female broodstock

reproductive quality. Results from the current study clearly demonstrate that,

selecting for a broodstock strain with fast growth and better reproductive

performance can be achieved successfully at the same time.

iv Genetic Improvement of the Pacific White Shrimp (Penaeus vannamei) in China

Table of Contents

ContentsKeywords................................................................................................................................... i

Abstract..................................................................................................................................... ii

Table of Contents..................................................................................................................... iv

List of Figures.......................................................................................................................... ix

List of Tables............................................................................................................................xi

List of Abbreviations..............................................................................................................xiv

Statement of Original Authorship........................................................................................ xviii

Acknowledgements................................................................................................................ xix

Chapter 1: Introduction....................................................................................... 1

1.1 Aquaculture.....................................................................................................................2

1.2 Penaeid Shrimp Farming.................................................................................................3

1.3 Penaeus vannamei...........................................................................................................4

1.3.1 Geographic Distribution and Global Production.................................................. 4

1.3.2 Production Lifecycle of P. vannamei................................................................... 6

1.4 Genetic Breeding of Penaeid Shrimps............................................................................ 9

1.4.1 Basic Concepts in Genetic Breeding.................................................................... 9

1.4.2 Domestication History of Penaeid Shrimps........................................................12

1.4.3 Status of Stock Improvement of Penaeid Shrimp...............................................13

1.4.4 Genetic Parameters and Genetic Gains in Selected Traits in PenaeidShrimp.................................................................................................................16

1.5 Bridging the Gap between Population Genetics/Genomics and Quantitative Genetics28

1.5.1 Molecular Markers..............................................................................................28

1.5.2 Parentage Assignment........................................................................................ 30

1.5.3 Quantitative Trait Loci (QTL) Mapping.............................................................31

1.5.4 Genomic Selection..............................................................................................32

Genetic Improvement of the Pacific White Shrimp (Penaeus vannamei) in China v

1.5.5 Whole Genome Sequencing of Aquatic Species................................................ 33

1.6 Issues with P. vannamei Broodstock Quality in China.................................................35

1.6.1 Limitation of the Imported SPF Broodstock...................................................... 35

1.6.2 Inbreeding........................................................................................................... 36

1.6.3 Issues of Base Population................................................................................... 37

1.7 Aims of the Current Project.......................................................................................... 38

1.8 Objectives and Thesis Outline...................................................................................... 39

1.8.1 Chapter (1): General Introduction.......................................................................39

1.8.2 Chapter (2): Characterization for the Culture Resources of Pacific WhiteShrimp of Genetic Diversity, Genetic Structure, and Genetic Relatedness........39

1.8.3 Chapter (3): Genetic Parameters for Body Weight and Survival in theBase Population.................................................................................................. 39

1.8.4 Chapter (4): Comparison of Reproductive Performance of Female PacificWhite Shrimp Reared in Recirculating Tanks vs Earthen Ponds....................... 40

1.8.5 Chapter (5): Quantitative Genetic Analysis of Female ReproductiveTraits................................................................................................................... 40

1.8.6 Chapter (6): General Discussion.........................................................................41

Chapter 2: Levels of Genetic Diversity and Inferred Origins of Penaeusvannamei Culture Resources in China: Implications for the Production of aBroad Synthetic Base Population for Genetic Improvement................................43

2.1 Introduction...................................................................................................................46

2.2 Materials and Methods..................................................................................................49

2.2.1 Sampling............................................................................................................. 49

2.2.2 DNA Extraction and Genotyping....................................................................... 52

2.2.3 Data Analysis......................................................................................................52

2.3 Results...........................................................................................................................56

2.3.1 Genetic Diversity and HWE Estimates...............................................................56

2.3.2 Population Genetic Differentiation.....................................................................59

2.3.3 Relatedness Estimates.........................................................................................65

2.3.4 Effective Population Size (Ne)........................................................................... 67

2.4 Discussion..................................................................................................................... 67

2.4.1 Genetic Variation Levels Within and Among Stocks.........................................67

2.4.2 Population Differentiation and Origins of Genetic Resources........................... 70

2.4.3 Implications for Forming a Genetic Foundation (Base) Population forGenetic Improvement......................................................................................... 74

vi Genetic Improvement of the Pacific White Shrimp (Penaeus vannamei) in China

2.5 Conclusions...................................................................................................................76

Chapter 3: Genetic parameters for growth and survival traits in a basepopulation of Pacific white shrimp (Penaeus vannamei) developed fromdomesticated strains in China..................................................................................77

3.1 Introduction...................................................................................................................79

3.2 Materials and Methods..................................................................................................83

3.2.1 Animal Material and Crossing Design............................................................... 83

3.2.2 Broodstock Management.................................................................................... 85

3.2.3 Synthesis of Families.......................................................................................... 86

3.2.4 Larviculture.........................................................................................................87

3.2.5 VIE Tagging....................................................................................................... 88

3.2.6 Growth Rate and Survival Experiment...............................................................88

3.2.7 Statistical Analysis..............................................................................................89

3.3 Results...........................................................................................................................91

3.3.1 Survival in Experimental Tanks......................................................................... 91

3.3.2 Descriptive Statistics.......................................................................................... 91

3.3.3 Genetic Analysis of Growth and Survival Traits................................................94

3.4 Discussion..................................................................................................................... 95

3.4.1 Experimental Tank System.................................................................................96

3.4.2 Genetic Parameters for Growth and Survival Traits...........................................97

3.4.3 Genetic Correlations between Growth and Survival........................................ 100

3.4.4 Effects of Strain on Growth and Survival.........................................................101

3.4.5 Implications for Further Study..........................................................................102

3.5 Conclusions.................................................................................................................105

Chapter 4: Comparison of Reproductive Performance of Domesticated P.vannamei Females Reared in Recirculating Tanks and Earthen Ponds: AnEvaluation of Reproductive Quality of Spawns in Relation to Female Body Sizeand Spawning Order...............................................................................................107

4.1 Introduction.................................................................................................................111

4.2 Methods and Materials................................................................................................117

4.2.1 Experimental Animal........................................................................................117

4.2.2 Broodstock Rearing Procedure in Earthen Ponds.............................................117

4.2.3 Broodstock Rearing Procedure in Recirculating Tanks....................................118

4.2.4 Design for Experimental Comparisons.............................................................119

Genetic Improvement of the Pacific White Shrimp (Penaeus vannamei) in China vii

4.2.5 Evaluation of Reproductive Parameters........................................................... 120

4.2.6 Statistical Analysis............................................................................................121

4.3 Results.........................................................................................................................122

4.3.1 Reproductive Performance in Relation to Treatment (RT vs EP).................... 122

4.3.2 Effect of Body Size on Individual Reproductive Performance........................ 126

4.3.3 Reproductive Parameters in Relation to Spawning Order................................127

4.4 Discussion................................................................................................................... 128

4.4.1 Comparative Reproductive Performance of Broodstock in the RT and EPTreatments........................................................................................................ 129

4.4.2 Impacts of Female Body Size on Reproduction Performance..........................131

4.4.3 Quality of Reproductive Performance in Relation to Spawning Order............ 133

4.5 Conclusions.................................................................................................................135

Chapter 5: Quantitative Genetic Assessment of Female Reproductive Traitsin a Domesticated Pacific White Shrimp (Penaeus vannamei) Line in China...137

5.1 Introduction.................................................................................................................140

5.2 Materials and methods................................................................................................ 144

5.2.1 Experimental Families...................................................................................... 144

5.2.2 Measurement of Reproductive Traits............................................................... 144

5.2.3 Statistical Analysis............................................................................................145

5.3 Results.........................................................................................................................148

5.3.1 Descriptive Statistics........................................................................................ 148

5.3.2 Relationships Between Body Weight and Number of Eggs/Nauplii perSpawn............................................................................................................... 150

5.3.3 Frequency Distribution of Number of Females Spawning............................... 151

5.3.4 Genetic (Co)variances Among Traits............................................................... 152

5.3.5 Genetic and Phenotypic Correlations among Reproductive Traits...................153

5.4 Discussion................................................................................................................... 156

5.4.1 The Experiments...............................................................................................156

5.4.2 Heritability Estimates....................................................................................... 158

5.4.3 Genetic and Phenotypic Correlations............................................................... 160

5.4.4 Implication for Selection Programs.................................................................. 163

5.5 Conclusions.................................................................................................................165

Chapter 6: GENERAL DISCUSSION............................................................166

viii Genetic Improvement of the Pacific White Shrimp (Penaeus vannamei) in China

6.1 Characterization of Pacific White shrimp genetic diversity and genetic structure inChina........................................................................................................................... 167

6.2 Genetic parameters for body weight and survival in the base population.................. 170

6.3 Comparison of reproductive performance of female Pacific white shrimp reared inrecirculating tanks vs earthen ponds........................................................................... 171

6.4 Quantitative genetic analysis of female reproductive traits........................................ 174

6.5 Future direction for Pacific white shrimp breeding programs.................................... 176

6.5.1 Breeding Strain for AHPND Disease Resistance............................................. 176

6.5.2 Genome Selection (GS).................................................................................... 177

6.5.3 Dissemination of the Improved Pacific White Shrimp Stock...........................178

6.6 Concluding Thoughts..................................................................................................181

References................................................................................................................183

Appendices...............................................................................................................239

Genetic Improvement of the Pacific White Shrimp (Penaeus vannamei) in China ix

List of Figures

Figure 1.1 The natural distribution of Penaeus

vannamei…………………………….5

Figure 1.2 Schematic diagram depicting stages in the general commercial production

cycle for Penaeus vannamei………………………..……………………………….8

Figure 2.1: A bar plot showing mean number of alleles (Ā) for 36 breeding

lines….55

Figure 2.2: An unrooted neighbour joining tree for 36 P. vannamei breeding lines

based on seven microsatellite loci using Nei’s DA genetic distance

method…………………………………………………………………….…60

Figure 2.3: Individual assignment based on Bayesian analysis of 36 breeding lines at:

a) Structure plot for K=2; b) Structure plot for K=4…………………….61

Figure 3.1 a, b) Maturation tank system used in the experiment; c, d) selecting

candidate females with ovarian development at IV ~ V stage for artificial

insemination…………………………………………………………………84

Figure 3.2 a) 500L tanks used for families reared separately (with capacity to

produce 245 families each breeding cycle); b) collecting nauplii (cloudy

white area) for the next larviculture step; c) larviculture for families reaching

Z2~Z3 stage; d) family successful reaching PL

stage…………………………………………82

Figure 4.1 a) Earthen ponds for experimental broodstock trials; b) shrimp after a

five month culture period (size of 20.0 ~ 25g); c) shrimp at eight months; d)

x Genetic Improvement of the Pacific White Shrimp (Penaeus vannamei) in China

packaged broodstock in 10 L nylon bags (temperature at ~ 18 °C ) and

transferred to hatchery for reproductive traits

test……………………….…113

Figure 4.2 Test females subjected to unilateral eyestalk

ablation………..…………115

Figure 4.3 a) Pie charts showing the number of spawns for 101 females P. vannamei

broodstock in the recirculating tank treatment (RT) over a one month trial (SF,

number of spawning events); b) Number of spawns for 45 females in the

earthen pond treatment (EP) over a one month trial (SF, number of spawning

events)……………………………………………………………………...119

Figure 5.1 Relationship between body weight after spawning (WAS) and number of

eggs per spawn (NE)…………………………………………………..…...141


nauplii per spawn (NN)…………………………………….………………142

Figure 5.3 Number of spawns for 595 females over the 30 day trial (SF, number of

spawning record)………………………………………………………...…143

Figure S2.1 The estimated delta values illustrate the most likely number of

subpopulations (K = 2 and K = 4) based on Bayesian

assignment…………227

Genetic Improvement of the Pacific White Shrimp (Penaeus vannamei) in China xi

List of Tables

Table 1.1 Existing domestication programs for penaeid shrimps across the

world………………………………………………………………………...15

Table 1.2 Summary of heritability estimates (h2±SE) and genetic gains for growth

and size-related traits in penaeid shrimps………………………….…..……18

Table 2.1 Penaeus vannamei sample information……………………………….…49

Table 2.2 Genetic diversity measures for 36 batches of P. vannamei broodstock

(N=1162) from 22 hatcheries in China based on 7 microsatellite loci……...54

Table 2.3 Population genetic differentiation among 36 P. vannamei stocks……….58

Table 2.4 Average relatedness estimates amongst 36 P. vannamei stocks…………63

Table 3.1 Number of families produced from 16 complete diallel crosses between

four Penaeus vannamei strains (NA_1, SA_1, KONA, and

LA)……………….…82

Table 3.2 Descriptive statistics for body weight at two different stages (BW1 and

BW2) and survival (S)………………………………………………………88

Table 3.3 Summary of analysis of variance for fixed effects (F-statistic value and

significant level)………………………………………………………..……88

Table 3.4 Estimated means for four purebred strains and six crosses for body weight

(g) at two stages (BW1 and BW2) and survival (S %)

……………………..89

xii Genetic Improvement of the Pacific White Shrimp (Penaeus vannamei) in China

Table 3.5 Estimates of variance components (σ2p, the phenotypic variance; σ2a, the

additive genetic variance; σ2e, the random residual error variance),

heritabilities (h2, ratio of additive genetic variance; e2, ratio of random

residual error variance), and genetic correlations for the body weight (BW1

and BW2) and survival (S) traits based on univariate animal model

analysis………………91

Table 4.1 Comparison of reproductive performance (plus standard errors) of P.

vannamei broodstock reared in two different treatments: earthen ponds (EP)

vs recirculating tanks (RT). Bold type indicates a significant difference

(p<0.05)……………………………………………………………………118

Table 4.2 Comparison of mean reproductive performance (plus standard errors) of

different size classes of female broodstock reared in earthen ponds (EP) vs

recirculating tanks (RT). Superscript letters indicate significant differences

within and between treatments (rearing conditions) for each reproductive

parameter…………………………………...................................................121

Table 4.3 Comparison of mean reproductive parameters (plus standard errors) for

different spawning frequency (spawn once only (1), twice only (2), or three

or more times (3+)) for female broodstock reared in earthen ponds (EP) vs

recirculating tanks (RT). Superscript letters indicate significant differences

within and between treatments (rearing conditions) for each reproductive

parameter……………………………………………………………..……122

Table 5.1 Descriptive statistics of reproductive traits for female Penaeus

vannamei……………………………………………………………………….…..140

Genetic Improvement of the Pacific White Shrimp (Penaeus vannamei) in China xiii

Table 5.2 Estimates of variance components (σ2p, phenotypic variance; σ2a, additive

genetic variance; σ2e, random residual error variance), and heritability

estimates (h2, ratio of additive genetic variance; e2, ratio of random residual

error variance) for WAS, NE, NN, HR, HRat, FE, and SF based on univariate

animal model analysis…………………………………….…………..……144

Table 5.3 Estimated genetic (below diagonal) and phenotypic correlations (above

diagonal) for body weight at spawning and reproductive traits* (estimates ±

se)………………………………………………………………………......145

xiv Genetic Improvement of the Pacific White Shrimp (Penaeus vannamei) in China

List of Abbreviations

A Number of alleles

AHPND Acute Hepatopancreatic Necrosis Disease

AI Artificial insemination

ANCOVA Analysis of Covariance

ANOVA Analysis of variance

Ar Allelic richness

BLUP The best linear unbiased prediction

BW Body weight

CAPPMA The China Aquatic Products Processing and Marketing Association

CN China

EMS Early mortality Syndrome

EP Earthen ponds

Fis Inbreeding coefficient

FAO Food and Agriculture Organization of the United Nations

FE The relative fecundity of number of eggs per g of female

G×E Genotype-by-Environment Interactions

GIFT Genetic improvement of farmed tilapia

Genetic Improvement of the Pacific White Shrimp (Penaeus vannamei) in China xv

GIH Gonad inhibiting hormone

GS Genome selection

GV Genetic variation

GWAS Genome-wide association study

h2 Heritability

HR The hatch rate of eggs

He Expected heterozygosity

Ho Observed heterozygosity

HWE Hardy-Weinberg Equilibrium

KONA The Kona line

LA Latin America

LE Linkage Equilibrium

mtDNA Mitochondrial DNA markers

M1-M3 Mysis stage 1 – Mysis stage 3

MAS Marker assisted selection

MCMC Markov Chain Monte Carlo

N1-N6 Nauplii stage 1 – Nauplii stage 6

NA North America

Ne Effective population size

xvi Genetic Improvement of the Pacific White Shrimp (Penaeus vannamei) in China

NE The number of eggs per spawn

NGS Next generation sequence

NN The number of nauplii per spawn

PAr Private allele richness

PIC Polymorphism information content

PL Post larvae

QTL Quantitative trait locus

rg Genetic correlations

rp Phenotypic correlations

rxy Estimated relatedness

R The response to selection

REML Restricted Maximum Likelihood

RT Recirculating tanks

S The selection intensity

SA Southeast Asia

SE The standard error

SF Spawn frequency

SNP Single nucleotide polymorphisms

SPF Specific Pathogen Free

Genetic Improvement of the Pacific White Shrimp (Penaeus vannamei) in China xvii

SPR Specific Pathogen Resistant

SSR Microsatellite markers

TSV Taura syndrome virus

VA Additive genetic variance

VD Dominance genetic variance

VE Effects of environments variance

VG Effects of genetic variance

VG×E The interaction effects between genetic and environment

VI Epistatic genetic variance

VP Phenotypic variance

VIE Visible implant elastomer tags

VIH Vitellogenesis inhibiting hormone

WAS Body weight after spawning

WSSV White spot syndrome virus

Z1-Z3 Zoea stage 1 – Zoea stage 3

xviii Genetic Improvement of the Pacific White Shrimp (Penaeus vannamei) in China

Statement of Original Authorship

The work contained in this thesis has not been previously submitted to meet

requirements for an award at this or any other higher education institution. To the

best of my knowledge and belief, the thesis contains no material previously

published or written by another person except where due reference is made.

Signature:

Date: _________________________

QUT Verified Signature

Genetic Improvement of the Pacific White Shrimp (Penaeus vannamei) in China xix

Acknowledgements

Foremost, I would like to express my sincere thanks to my primary supervisor, Dr

David Hurwood, without whom this PhD would not be possible. Thanks for his

patience and understanding to help me through this journey that allowed me to fulfill

the challenge and endeavour. I would also like to thank my associate supervisor, Dr

Peter Prentis who provided valuable laboratory and experimental advice for the

genome research component at QUT. Many thanks to my external supervisor Prof.

Peter Mather, without his encouragement this adventure would not have come true.

The whole story of my PhD began six years ago after meeting at Wuhan, China with

Peter. I would like to appreciate his excellent mentoring, always positive attitude and

for inspiring me. Special thanks also to Dr Yutao Li (CSIRO) at University of

Queensland for her help with statistical data analyses in Chapter (3).

Secondly, I would like to offer my thanks to our industry partner in Beijing, China

- Shuishiji Pty. Ltd. for providing the facilities for all field experiments in China for

the project. Special thanks are due to Shuishiji Chief Manager, Mr. Tang. We have

had a six year journey of cooperation on genetic breeding of shrimp, faced many

significant issues over the time and finally resolved all of the problems to complete

this long journey. I also would like to thank Queensland University of Technology

(QUT) for providing a Post Graduate Research Award (QUTPRA) to undertake my

PhD research.

Thanks are also due to Mr. Abing Gao from the Shrimp Hatchery Association of

Xiamen, China for sharing his valuable knowledge about the history and origins of

Pacific white shrimp broodstock and farming in Fujian Province. Mr. Jian Tan,

xx Genetic Improvement of the Pacific White Shrimp (Penaeus vannamei) in China

manager of the hatchery at Zhanjiang also provided generous assistance with

sampling for the Chapter (2) study and shared data on the history of breeding line

origins of the broodstock assembled there. I would also like to thank my friends Mr

Huixiao Zhang and Tao Lu for their help and assistance in collecting samples for the

chapter (2) study. Mr. Junjie Huang provided assistance with field pond management

for the broodstock experimental trials in Chapter (4). Thanks to Mr. Bin Liao and Mr

Tao Lu for their assistance with larvae culture, VIE tagging, and recirculation tank

management. Thanks are also due to Mr Xuehua Pi and Mr Zhikai Xu for their

assistance with data collecting for Chapters (3) and (5).

I would also like to thank my QUT laboratory mates; Kim Rogl, Md. Lifat Rahi,

Dania Aziz, Azam Moshtaghi, Mitchell Irvine, Pia Schoenefuss and Liam Bartlett,

for sharing their knowledge, advice and lovely coffee times. It was a memorable set

of friendships during my PhD journey and will be treasured for life.

Last but not least, I would like to express my deep thanks to my wife Bing Xu for

her selfless love and sacrifices to her career. Without this unconditional love and

support, I could not have completed this PhD journey. Thanks also to my darling

little daughters; Judy Ren and Sandy Ren, I always have lovely time at home. Thanks

to my brother Shengying Ren for your encouragement and support, and thanks to my

parents for your love.

Chapter 1: Introduction 1

Chapter 1: IntroductionHow inappropriate to call this planet Earth, when clearly it is Ocean. --- Arthur C. Clarke

Domestication of plants and animals was a major development in agriculture that

proved a significant milestone in the evolution of human civilization. The earliest

records of animal and plant domestication date to around 12,000 years bp (the

Terminal Pleistocene period) and occurred in nine different areas across the world

(Matsui et al., 2005; Zeder, 2008; Zeder et al., 2006). Human civilization was

changed by this development as hunter-gatherers became sedentary and established

settlements with domesticated plants and animals providing the main source of

calories and nutrients. The subsequent emergence of agriculture enabled humans to

have reliable food sources and encouraged development of early urban villages

(Bar‐Yosef, 1998; Clutton-Brock, 1999; Driscoll et al., 2009). Together, these events

became known as the ‘Neolithic Revolution’ (Bocquet-Appel, 2011; Lewin, 2009).

Over a long history of more than 9,000 years, a number of terrestrial animal and

plant species have been domesticated successfully, but even today this only relates to

approximately 0.08% of known land plant and 0.0002% of known land animal

species (Duarte et al., 2007; Groombridge and Jenkins, 2000). 94% of livestock food

production currently is based on only five major domesticated mammalian species

(Liao and Huang, 2000). In contrast, domestication of most aquatic species is a

relatively recent phenomenon with the majority of species not domesticated until

well after the start of the 20th century. This practice however, has grown

exponentially in recent times with more than 430 aquatic species now farmed in

aquaculture (Duarte et al., 2007; Teletchea and Fontaine, 2014). The next wave of

2 Chapter 1: Introduction

domestication that focused on aquatic species primarily for human food is referred to

as the “blue revolution” and is directed at providing the world with nutrition for

growing human populations and food security to meet increased demand for

relatively low cost, animal protein (Ahmed et al., 2018; Béné et al., 2016; Harlan et

al., 2012).

1.1 Aquaculture

One of the planet’s most significant challenges currently, is how to feed more than

9 billion people by 2050 in a setting of climate change, growing competition for

natural resources, and economic uncertainty (FAO, 2016a). Compared with

terrestrial domestic farm animals, aquatic animals are more efficient converters of

energy to protein, offer high nutritional value, and are rich in healthy omega-3 fatty

acids (Gjedrem et al., 2012). It is now clear therefore, that aquaculture will make an

increasingly vital contribution to future world food supply (FAO, 2016a; Gjedrem

and Robinson, 2014; Gjedrem et al., 2012).

Over the period of 2000 - 2012, farming of aquatic species has expanded rapidly

resulting in aquaculture now being the fastest expanding animal-food production

sector around the world, with an annual rate of increase of 6.2% (FAO, 2014). In

2013, fish contributed approximately 17% to total animal protein consumed by

global human populations (FAO, 2016a) but a significant milestone was reached in

2014 when the aquaculture sector’s contribution to supply of fish for human

consumption overtook that coming from wild-caught fish. It is expected that fisheries

will pass another milestone over the next decade when animal protein supply from

aquaculture production is predicted to overtake traditional terrestrial meat industry

supply from livestock and poultry (FAO, 2012; 2016a).


The Asia-Pacific region, that in 2016 was home to more than 4.5 billion people

(containing more than 60% of the world’s population), currently contributes more

than 90% to total global aquaculture production, the majority of which is produced in

China (FAO, 2016b). Aquaculture supplies over 20% of total protein intake by

people across this region but this is growing rapidly (nearly 10% annually).

Aquaculture now makes a significant contribution not only to food security, but also

to human nutrition, improving rural livelihoods and economic growth across this

region (FAO, 2016a; b). Among hundreds of aquaculture species farmed in this

region, non-native species make a remarkable contribution to seafood production. In

particular in Asia, Pacific white shrimp is the pre-eminent farmed species in terms of

trade value (Kumar and Engle, 2016).

1.2 Penaeid Shrimp Farming

Penaeid shrimp are currently the most successful farmed aquaculture commodity

around the world. Of the 110 species in 12 genera that are members of the family

Penaeidae, the most commercially important farmed shrimps include: Penaeus

(Litopenaeus) vannamei Boone, 1931, P. monodon Fabricius, 1798, P.

(Fenneropenaeus) chinensis (Osbeck, 1765), P. (Fenneropenaeus) indicus Milne-

Edwards, 1837, P. (Fenneropenaeus) merguiensis de Man, 1888, P. (Litopenaeus)

stylirostris Stimpson, 1874 and P. (Marsupenaeus) japonicus Bate, 1888

(Andriantahina et al., 2013b; Benzie, 2009). P. vannamei and P. monodon in

particular, are the most widely farmed species in tropical and sub-tropical areas.

Production of these two species currently provide 90% of the world’s farmed marine

shrimp (FAO, 2018a; Moss and Moss, 2009). In 2001, production of farmed shrimp

overtook wild capture production (Primavera, 1998). In 2016, more than 60% of

global shrimp production came from farmed shrimp estimated at more than 5 million


tonnes, which equated to >32 billion USD in trade value (FAO, 2018a). In general,

shrimp contribute one of the highest values per unit weight of any aquaculture

commodity, with approximately 3.9-fold and 4.7-fold higher value than fish and

molluscs, respectively (Benzie, 2009).

1.3 Penaeus vannamei

1.3.1 Natural Distribution and Global Production

Natural distribution

P. vannamei, commonly referred to as Pacific white shrimp or whiteleg shrimp, is

native to the tropical Pacific coast of the Americas from north of the Gulf of

California in Sonora, Mexico to northern Peru (Briggs, 2005) (Figure 1.1). While to

date, there is only very limited data available from earlier studies that have

investigated the natural population structure of Pacific white shrimp (Valles-Jimenez

et al., 2004; Valles-Jimenez et al., 2006), these studies have suggested that wild

populations may be structured spatially. While P. vannamei is primarily marine it is

unusual among other penaeid taxa because it can acclimate to a wide range of

salinities and is able to tolerate environmental salinities from pure fresh water (0 ppt)

to hyper-marine conditions (45 ppt) (Araneda et al., 2008; Bray et al., 1994).


Figure 1.1 The natural distribution of Penaeus vannamei.

From FAO: http://www.fao.org/figis/geoserver/factsheets/species.html

Global production

A number of characteristics of this species make it well suited to farming,

including: i) ease of reproduction in captivity, ii) tolerance of high stocking densities,

iii) high productivity per culture unit, iv) tolerance of variation in water salinity, v)

low animal protein requirement in feed, and vi) availability of genetically improved

seed (Briggs, 2005; Moss et al., 2009). Since it was first introduced into commercial

production in Asia in 2000, contribution from farming P. vannamei has increased

from an initial ~10% of global shrimp (Moss and Moss, 2009) to 4.16 million tons

today, which constitutes more than 80% of total world production of farmed shrimp

(FAO, 2018a). Among traded seafood commodities currently, P. vannamei ranks

http://www.fao.org/figis/geoserver/factsheets/species.html


first in value among aquaculture species that contributed 24.40 billion USD to the

global market in 2016 alone (FAO, 2018a).

1.3.2 Production Lifecycle of P. vannamei

There are three basic stages in the lifecycle when farming P. vannamei: i)

broodstock maturation in hatchery tanks; ii) larval-culture in nursery tanks; and iii)

growout in commercial ponds (Figure 1.2). Broodstock used for seed supply in

commercial P. vannamei production come from three main sources. Wild

populations from sea-capture (size of broodstock usually >40g at approximately 1

year in age) were used as the original source for broodstock. Historically, wild

broodstock had been popular because they were thought to produce higher quality

nauplii than those from farmed stocks, and that they were also easier to manage in

larval-culture (Browdy 1998). However, up until the late 1990s, broodstock from

wild stocks were not used extensively by the commercial seed sector due to both

biosecurity concerns and development of technology for the maturation process for

domesticating the species (Briggs, 2006; Cock et al., 2017; Cock et al., 2009).

Alternatively, small entrepreneurs in Asia and Latin America prefer maturing

cultured shrimp taken from growout ponds.

In practice, shrimp from ponds at 4-6 months of age and 15-25 g in weight used to

be transferred to maturation tanks in the hatchery. After 2-3 months of pre-

maturation rearing, these shrimp were used for nauplii production (Briggs et al.,

2004). Currently, tank-reared Specific Pathogen Free (SPF) or Specific Pathogen

Resistant (SPR) broodstock are preferred; this type of broodstock are from

genetically improved individuals that are sourced from improvement programs

(Alday‐Sanz et al., 2018; Cock et al., 2017; Moss et al., 2012a). Broodstock at 8-10


months of age after purchase are usually sourced from international breeding

companies and supplied to local hatcheries by air.

Female Pacific white shrimp at 8-10 months of age can be induced to reproduce

by unilateral eyestalk ablation that can trigger repeated maturation and multiple

spawning events. The period for female nauplii production lasts approximately 3

months, with 5 to 15% of females spawning per night in commercial hatcheries.

In larval-culture, nauplii develop through a number of larval stages that include

six non-feeding nauplii stages (N1-N6), three feeding zoea stages (Z1-Z3), three

mysis stages (M1-M3) following which they reach the post-larval (PL) stage.

Generally, it takes ~20 days for nauplii in culture to reach the PL10-12 stage, which

is required by farmers for growout. Feeding during larval culture stages normally

includes mixed ingredients of live food (microalgae and Artemia) and artificial

micro-encapsulated feeds (~40% crude protein content).

Depending on the pond conditions and the management practices employed,

shrimp farming types can be divided into four main categories that include: extensive,

semi-intensive, intensive and super-intensive, with each approach applying different

stocking densities. Well-managed shrimp in a pond can reach market size within

three months, and are usually harvested at ~ 90 days.


Figure 1.2 Schematic diagram depicting stages in the general commercial productioncycle for Penaeus vannamei. From FAO:http://www.fao.org/fishery/culturedspecies/Penaeus_vannamei/en


1.4 Genetic Breeding of Penaeid Shrimps

1.4.1 Basic Concepts in Genetic Breeding

1.4.1.1 Genetic Variance

The fundamental tool for production improvement of penaeid shrimp is based on

quantitative genetics. Genetic breeding is an artificial breeding strategy that exploits

the additive genetic variance (VA) that is present in a breeding line (Gjedrem, 2005).

Variance in phenotypic values (VP) however, is influenced by both effects of genetic

variance (VG), environmental effects (VE), and the interaction between these factors

(VG×E) (Falconer, 1960), such that:

VP = VG + VE + VG×E.

VG is the most useful source of variation for breeders and can be further

partitioned into three components: additive genetic variance (VA), dominance genetic

variance (VD), and epistatic genetic variance (VI):

VG = VA + VD + VI.

VA can be transferred from parents to progeny via inheritance, but while other

components are also generally considered to be inherited, their inheritance patterns

cannot be predicted. (Falconer and Mackay, 1996). Therefore, the amount of

phenotypic variance that is due to VA in the breeding line primarily determines the

response of the population to selection. Hence, the focus of selection in animal

breeding is on the statistical partitioning of phenotypic variance in the breeding line

into additive genetic and environmental components (Hickey et al., 2017b).

1.4.1.2 Heritability

Heritability (h2) is defined as the percentage of VP that is inherited in a predictable

manner. In the narrow sense, it can be described as the ratio between VA and VP

(Falconer and Mackay, 1996):

h2= VA/VP.


Estimates of h2 on target phenotypic traits are essential when initiating a breeding

program, to calculate breeding values for candidate broodstock individuals and to

predict the genetic response to selection (Gjedrem, 2005). Estimates of heritability

range from 0 -1 and in general, traits with h2<0.15 are considered to show low

heritability which implies that these traits are likely to be difficult to change using a

selection approach (Cassell, 2009; Tave, 1986). Traits with h2 between 0.15 and 0.4

are considered to show moderate heritability, and traits with h2 greater than 0.4 show

high heritability; moderate and high heritability traits should respond well to

selection. When the h2 for a target trait is known and the selection intensity (S) is set,

the response to selection (R) can be predicted:

R = Sh2.

1.4.1.3 Methods of Selection

Methods for genetic selection can be divided into two major types, largely

depending on whether pedigree information is available or not: individual or mass

selection (without pedigree) and family selection (with pedigree). Family-based

selection schemes can also be split into three sub-types: between family selection,

within family selection, and combined selection. To determine the most appropriate

selection method however, for a specific genetic breeding program, the available

genetic variation in a target stock should be considered in association with levels of

heritability of the desired traits to be subjected to selection. Furthermore, recording

methods, the capacity of available facilities, as well as the reproductive

characteristics of the target species should also be considered (Fjalestad, 2005).

There are both advantages and disadvantages associated with each method of

selection.


Individual, or mass selection is based on individual performance (i.e. individual

phenotype) (Fjalestad, 2005). This method is popular because it does not incur high

costs associated with developing a pedigree record system. It simply relies on

selecting superior individuals from a population that are grown en mass. Therefore

the overall demands for facility capacity and the budget for mass selection are

relatively economical. Mass selection however, can only target traits that can be

measured on living candidate individuals and are unlikely to be effective when traits

with low heritability are targeted. For such traits, environmental and maternal effects

usually play dominant roles in determining phenotype and, as these factors are not

heritable, this results in poor genetic gain responses. Moreover, where pedigree

information is not available, mass selection is likely to lead to rapid accumulation of

high inbreeding risks in particular in highly fecund species as is the case with most

aquatic species.

Family-based methods in general, provide a better option than mass selection

where spawning of aquatic species can be controlled, when commercial traits with

low heritability are targeted, or when traits cannot be measured on live individuals

(e.g. flesh colour or disease resistance). This approach however, requires a pedigree

record system to be maintained for each family and extensive facilities to maintain

and rear progeny from each family, separately.

The between-family selection approach requires selecting breeding candidates

based on family ranks of mean trait values; those with the best means are selected for

mating to produce progeny for the next breeding cycle. This approach requires

retention of numerous families that are communally reared in a single large holding

environment marked with an efficient pedigree record system so that the breeding

value of each family can be estimated by the Best Linear Unbiased Prediction (BLUP)


approach or other similar algorithms e.g. Restricted Maximum Likelihood (REML)

or Bayesian inference via Markov Chain Monte Carlo (MCMC) methods (Gianola

and Rosa, 2015). These methodologies require substantial infrastructure resources to

be maintained for an appropriate number of families. Another potential negative is

that this approach may evolve biases when unequal numbers of individuals are

present among families.

Within-family selection assumes each family to be an independent sub-population,

with the best performing individuals in each sub-population selected as candidate

broodstock. This method is often used to minimise environmental effects when

families are reared separately. If sexual dimorphism is present in the target species,

selection on individuals should be undertaken separately for each sex.

A combined selection approach optimises the approaches of family and within

family selection and estimates family breeding values from both full and half sibs in

addition to that of each breeding candidate. Best performing individuals from the

best performing families are then selected to produce progeny for the new breeding

cycle. As a consequence, this method maximizes the genetic gain so it is generally

considered to be the best selection method. Inbreeding rates however, may

potentially accelerate if the contribution from each family is not equal; this is usually

dealt with actively in well-managed programs.

1.4.2 Domestication History of Penaeid Shrimps

Before family selection was initiated in the late 1990s, there had been a long

history of domestication of penaeid shrimps. In general however, this practice was

more ‘art’ rather than ‘science’ (Alday-Sanz, 2010). Even today, most commercial


shrimp larviculture management practices are similar to those used in the early 1930s

(Treece and Fox, 1993).

Dr. Motosaku Fujinaga (Hudinaga) is recognised as the pioneer of domesticated

penaeid shrimps. He was the first person to close the life cycle of wild-caught gravid

female P. japonicus and to rear larvae to the sub-adult stage under lab conditions in

1934 (Hudinaga, 1942). This method was the only way to for approximately 40 years

to domesticate penaeid species and to induce spawning in captivity until the

unilateral eyestalk ablation method was developed (Treece and Fox, 1993).

Knowledge gained by Dr. Fujinaga was instrumental in the development of a

global shrimp farming industry. When Japan’s domestication technology was

transferred to the USA, an important contribution towards domesticating penaeid

shrimps occurred at the National Marine Fisheries Laboratory in Galveston, Texas

(Fast and Lester, 2013). The technique was named the ‘Galveston Method’ and is

characterised by use of high larval culture densities, application of reliable water

management practices, and feeding both live algae and Artemia that together provide

high, predictable post larval survival in farms (Fast and Lester, 2013). Around the

same time, Dr. I Chiu Liao who studied under Dr. Fujinaga modified the

domestication technique developed for P. japonicus and applied it to P. monodon

production in Taiwan. This technique is now referred to as the ‘Taiwanese Method’

that shaped development of the modern shrimp farming industry in Asia.

1.4.3 Status of Stock Improvement of Penaeid Shrimp

The first genetic improvement project directed at penaeid shrimp culture was

initiated at the Oceanic Institute in Hawaii, USA. This institute developed the SFP

(specific pathogen-free) concept and in combination with genetic family selection,

promoted and shaped the modern shrimp farming industry by providing genetically


improved P. vannamei broodstock with fast growth performance and high health

status (Lightner et al., 2009b). Following this development over the last four decades,

penaeid shrimp genetic improvement programs have been developed in North and

South America, Asia, Australia and New Caledonia based on both family selection

and mass selection approaches. Specifically, these projects have focused primarily on

selecting for key commercial traits that include fast growth and improved disease

resistance (Gjedrem and Robinson, 2014).

While fully domesticated stocks are now available for all commercially important

penaeid species, in recent times the main focus has been on two species: P. vannamei

and P. stylirostris (Table 1.1) and genetically improved strains now provide seed to

some culture industries (Benzie, 2009). For the other five farmed penaeid species,

most breeding improvement programs are still at the research stage or have only

reached the early stages of commercial development and do not yet provide

improved broodstock to industry (Moss et al., 2009). Most commercial breeding

programs for penaeid species are based in the Western Hemisphere (Table 1.1). This

essentially matches geographical availability of the majority of the world’s

quantitative genetics expertise. The pattern is distorted however, because shrimp

culture in Asian countries accounts for 90% of global shrimp aquaculture but only a

very limited number of improved lines are available there (FAO, 2014). Currently,

there is an urgent need in the Asia-Pacific region, to establish stock improvement

programs for penaeid shrimps to meet growing regional and world demand for this

important commodity and to address critical problems that include; low relative

productivity in culture, poor biosecurity and poor product quality in an expanding,

competitive world market.


Table 1.1 Existing domestication programs for penaeid shrimps across the world

Species Regions of the breeding Generations Origin of the base Ownership Marketed References

Penaeus vannamei

Hawaii, USA (several) 27 Mexico and Ecuador Private International sales 1

Florida, USA 22+ Mexico and Ecuador Private International sales

Colombia 19+ Columbia, CostaRica, Ecuador,Hawaii, Panama,Peru, Salvador andVenezuela

Private/

government

Domestic sales 2

Mexico (several) Various upto 18+

Mexico, Ecuador,Venezuela,Colombia, andFlorida

Private Domestic sales 3,4

Venezuela 26 years Mexico, Panama,and Colombia

Private Private use 5

Brazil (several) Various upto 14+

Costa Rica,Ecuador, Panamaand Venezuela

Private Domestic sales 6

China (several) Unknown USA and SouthAmerica

Private/

government

Domestic sales 7

Thailand (several) Unknown USA and SouthAmerica

Private Domestic sales 7

Penaeus monodon

Hawaii, USA 12 Indo-Pacific Private International sales 8

Madagascar 24+ South-west IndianOcean

Private No 9

Australia 15+ East and north coastof Australia

Private/

Government

Domestic sales 10

Thailand 12+ Thailand waters Government Domestic sales 9

Thailand 10+ Thailand waters Private Domestic sales 9

China Unknown MozambiqueChannel, IndonesiaBanda Aceh,Thailand Khanom,and China Sanya

Government No 11

Vietnam Unknown Vietnam Rach Gocsea, Ca Mau

Government No 12

India 8+ India Tamil Naduand Andhra Pradesh

Government Unknown 13

Penaeus merguiensis Thailand 21 Andaman Sea Government Experimental,

domestic sales

9

Penaeus chinensis China 19 China Government Domestic sales 14

Penaeus japonicus Australia 17+ Australia Private Domestic sales 15

China 4 China Private Experimental 16

Penaeus stylirostris New Caledonia 42 Mexico and Panama Private Domestic 17


Hawaii, USA 21+ Ecuador Private International sales 17

Saudi Arabia Unknown Saudi waters private No 7

Penaeus indicus Iran unknown Persian Gulf Cooperative Domestic sales 7

Egypt 5+ Red Sea Government Experiment 18

References: 1. Argue et al., 2002; 2. Gitterle et al., 2005b; 3. Ibarra et al., 2007a; 4. Castillo-Juárez et

al., 2007; 5. De Donato et al., 2005; 6. Freitas et al., 2007; 7. Briggs et al., 2004; 8. Argue et al., 2008;

9. Benzie, 2009; 10. Macbeth et al., 2007; 11. Sun et al., 2015b; 12. Nguyen, 2009; 13. Krishna et al.,

2011; 14. Zhang et al., 2011; 15. Preston et al., 1999; 16. Liu et al., 2019; 17. Goyard et al., 2008b; 18.

Megahed et al., 2018.

1.4.4 Genetic Parameters and Genetic Gains in Selected Traits in Penaeid Shrimp

1.4.4.1 Breeding Traits in Penaeid Shrimp

Breeding goals in stock improvement programs should reflect the economic

importance of culture traits that are heritable and that can be measured accurately

(Gjedrem and Baranski, 2010; Gjedrem and Rye, 2016). A review of current stock

improvement programs for penaeid shrimp species shows that breeding goals can be

divided into four types that include programs directed at; growth traits, survival rate,

disease resistance, and reproductive traits.

1.4.4.2 Selection for Growth Related Traits

Growth rate is considered to be the most commercially important trait by most

farmers, because improving this trait can increase the number of harvests per year,

and/or increase the average size of individuals over the same culture period, thereby

resulting in higher market returns. Moreover, improving growth rate can also

improve other correlated commercial traits via indirect selection, including feed


conversion efficiency and survival rate (Caballero-Zamora et al., 2015b; Gjedrem

and Rye, 2016; Goyard et al., 2001). While growth can be expressed as an absolute,

relative, or specific rate, shrimp breeders usually express growth by “weight at age”

or use specific morphological characters as markers for growth (Table 1.2) (Hopkins,

1992). This is reasonable because harvest weight and growth rate generally show

high phenotypic and genotypic correlations (≥ 0.85) (Moss et al., 2009). Similarly,

breeders can also use morphometric correlates of weight as selection criteria instead

of measuring absolute shrimp weight where there is a high positive correlation

between the traits of interest. For instance, the genetic and phenotypic correlations

between body weight, carapace length, carapace width, and carapace height ranged

from 0.81 to 1.00 in a recent case study of P. monodon (Sun et al., 2015b). It is also

worthwhile to note that using morphometric correlates of weight can improve ease

and accuracy of data acquisition (Lutz, 2008).

A variety of heritability (h2) estimates for growth-related traits (i.e. body weight at

age, length, growth rate, etc.) have been published for penaeid species including for

P. vannamei, P. stylirostris, P. japonicas, and P. chinensis (Table 1.2). From 21

estimates of genetic heritability for growth related traits, the average h2 was 0.316.

After excluding some earlier published estimates because they were based on only a

small number of families, these studies illustrate that genetic parameters for growth-

related traits show moderate to high heritability overall (h2 ≥ 0.15) (Table 1.2). A

relatively high h2 estimate suggests a good potential response to selection because a

significant proportion of the phenotypic variation is genetic (Moss et al., 2009). This

should result in efficient family breeding programs that are consistent with the

genetic gains reported previously for growth rate in shrimp species after selection

(Table 1.2). As an example, Argue et al. (2002) reported that after a single


generation of selection in P. vannamei, selected lines grew 21% and 23% faster

respectively, than a control line without selection when tested in two farming

environments (race ways and ponds). Kenway et al. (2006) reported that body weight

of a selected line of P. monodon was 10% higher at 30 weeks of age while Hetzel et

al. (2000) reported genetic gains of 10.7% per generation for growth rate in P.

japonicus. In addition, selection response for P. stylirostris and P. chinienses were

21% (Goyard et al., 2002) and 18.6% (Sui et al., 2016) over five generations,

respectively.

Another important issue for breeders to consider is the time frame over which to

select for fast growth families. From heritability estimates for growth-related traits

(Table 1.2), a variety of time frames have been suggested for growth trait selection.

Heritability for growth traits in penaeid shrimps however, can be different at

different life stages because shrimp do not exhibit linear growth over their individual

life times, with growth itself linked to the moulting cycle. Moreover, phenotypic

correlations between body weight at different times can also vary (Caballero-Zamora

et al., 2015a; Campos-Montes et al., 2013). Therefore, if the ultimate goal of a

breeding program is to provide improved stock to the farmer, the best time to focus

on improvement is for increased market weight (Moss et al., 2009) and to identify

the period when genetic correlations with market weight are highest.


Table 1.2 Summary of heritability estimates (h2±SE) and genetic gains for growthand size-related traits in penaeid shrimps.

Species Number of

families

Trait h2±SE Geneticgains

References

Penaeus vannamei

Unknown Weight at ˜11g 0.42 ± 0.15 Unknown Carr et al., 1997

53 Weight at 16 weeks 0.84 ± 0.43 (race way)

1.19 ± 0.59 (pond)

21% onegeneration

Argue et al., 2002

37

37

Weight at 29 weeks

Total length 29 weeks

0.34 ± 0.18

0.28 ± 0.18

*

*

Pérez‐Rostro and Ibarra,2003a

430 (52-70pergeneration)

Weight at ˜20 g 0.24 ± 0.05 (line 1)

0.17 ± 0.04 (line 2)

*

*

Gitterle et al., 2005b

67-77 pergeneration

Weight at 130 days 0.24 -0.45 ( fordifferent models)

* Castillo-Juárez et al., 2007

59 Weight 150 days

Total length 150 days

0.515 ± 0.030

0.394 ± 0.030

10.70% onegeneration

Andriantahina et al., 2012a

150-300 pergeneration

Weight 28 days

Weight 130 days

0.13 ± 0.03

0.21 ± 0.04

*

*

Campos-Montes et al., 2013

77 and 190 Weight at 137 daysand 175 days

0.092 ± 0.082 (overall)

0.066 ± 0.050 (group)

*

*

Lu et al., 2016

77 and 190 Weight at 137 day and175 day

0.335 ± 0.087 2.30% onegeneration

Sui et al., 2016c

150 Weight at 130 days 0.19 ± 0.03 * Caballero-Zamora et al.,2015a

55-130 Weight at 154-179days

0.31 ± 0.06 (Normaltemperature)

0.11 ± 0.03 (Lowtemperature)

*

*

Li et al., 2015

67-77 Weight at 19 weeks 0.15 – 0.35 (NoWSSV)

0.09 – 0.11 (WithWSSV)

*

*

Caballero-Zamora et al.,2015b

46-93 Weight at 150-190days

0.00 - 0.38 (5generations)

0.21 (overall)

* Sui et al., 2016a

150 Body weight 127 days

Tail weight 127 days

Tail percentage

0.15 ± 0.08

0.16 ± 0.08

0.12 ± 0.04

* Campos-Montes et al., 2017

40 Weight of low density

Weight of high density

0.44 ± 0.09

0.43 ± 0.09

* Tan et al., 2017a

65 Weight at 6 weeks

Weight at 10 weeks

0.24 ± 0.08

0.35 ± 0.10

* Zhang et al., 2017


Weight at 14 weeks

Weight at 18 weeks

0.46 ± 0.09

0.38 ± 0.07

79 Weight at 20 weeks 0.42 ± 0.09 * Nolasco-Alzaga et al., 2018

Penaeus monodon

18 Weight at 57mg

Weight at 449 mg

0.12 ± 0.02 (sir)

0.56 ± 0.03 (dam)

0.10 ± 0.00 (sir)

0.39 ± 0.00 (dam)

*

*

Benzie et al., 1997

21 Total length 25 days

Total length 65 days

0.15 ± 0.06

0.07 ± 0.04

*

*

Jarayabhand et al., 1998

9-29 Weight at 30 weeks

Weight at 40 weeks

Weight at 50 weeks

0.55 ± 0.07

0.45 ± 0.11

0.53 ± 0.14

*

*

*

Kenway et al., 2006

19 Weight at 16 weeks

Weight at 24 weeks

Weight at 32 weeks

Weight at 44 weeks

0.45 ± 0.13

0.32 ± 0.13

0.23 ± 0.11

0.39 ± 0.15

*

*

*

*

Coman et al., 2010

54 Weight at 148 days 0.27 ± 0.07 * Krishna et al., 2011

51 180 days for:

Body length

Body weight

Carapace length

Carapace width

Carapace height

0.18 ± 0.01

0.24 ± 0.01

0.20 ± 0.01

0.15 ± 0.01

0.13 ± 0.01

*

*

*

*

*

Sun et al., 2015b

Penaeus stylirostris 8 Growth rate from ~ 5gto 17g

0.11 21% fivegeneration

Goyard et al., 2002

Penaeus japonicus

34 Weight at ~ 6 months 0.23 10.7% onegeneration

Hetzel et al., 2000

96 Weight at 45 days 0.19 ± 0.04 * Liu et al., 2019

Weight at 75 days 0.19 ± 0.04 *

Weight at 105 days 0.16 ± 0.05 *

Weight at 135 days 0.19 ± 0.05 *

Weight at 165 days 0.18 ± 0.04 *

Penaeus merguiensis 48 Weight at 140 days 0.41 * Phuthaworn et al., 2016

Penaeus chinensis 57-123 Weight at 150 -199days

0.18 18.6% fivegenerations

Sui et al., 2016a

* No records for genetic gain.


1.4.4.3 Selecting for Improved Survival Rate

Apart from growth rate, survival rate is another crucial factor influencing the

success of shrimp farming (Gjedrem and Rye, 2016; Thitamadee et al., 2016). This

trait is not only economically important, but also easy to estimate precisely by simply

calculating the number of surviving individuals in a pond. Survival rate under rearing

conditions however, shows extremely low heritability estimates. Estimates of

survival traits h2 over the last five years have ranged from 0 to 0.11, with an average

of 0.038 (Caballero-Zamora et al., 2015b; Campos-Montes et al., 2013; Li et al.,

2015). This indicates that response to selection for general survival traits is likely be

low and it will therefore be a challenge to improve pond survival rate via a family

selection approach (Gjedrem and Baranski, 2010). Selection for disease resistance

for the most serious diseases affecting penaeids is an alternative approach to

improving overall survival rates in culture.

1.4.4.4 Selecting for Disease Resistance

Diseases are a major constraint on shrimp production in aquaculture (Cock et al.,

2017; Cock et al., 2009; Lightner et al., 2009a). Due to the current prevalence of

Early Mortality Syndrome (EMS) in Asia, estimates of annual output in Thailand

declined 30 to 70% in 2013 and production losses in Malaysia reached US$ 1 billion

in 2011(FAO, 2014). Annual economic lost due to disease for the Asian shrimp

industry was estimated to be more than US$ 20 billion according to Fish Vet Group.

Rapid spread of new and newly emerging pathogens have made the shrimp industry

in Asia significantly vulnerable (Thitamadee et al., 2016). The most attractive

strategies for disease management include exclusion or eradication of diseases,

developing specific disease resistant strains (SPR) and the adoption of efficient


biosecurity practices on farm (Cock et al., 2017; Moss et al., 2012a). The purpose of

selection for disease resistance is to develop strains that possess genetically based

pathogen resistance or tolerance to specific diseases. This approach is favoured by

shrimp farmers because they do not have to provide additional management or invest

in more sophisticated culture facilities and practices apart from paying slightly higher

prices for SPR seed (Cock et al., 2009). Another advantage of developing disease

resistant strains is minimal negative impacts on the environment compared with

some alternative measures that can include use of antibiotics and/or chemical

treatments (Cock et al., 2009). Development of disease resistance in cultured penaeid

shrimp stocks is costly however, and requires long time frames. Therefore, before

starting selection for resistance to a target disease, there are several criteria that need

to be considered carefully: (1) does the disease cause severe damage? (2) are there

other existing solutions for controlling an epidemic? and (3) is there sufficient

heritability for the trait of resistance to the target pathogen (Cock et al., 2017; Cock

et al., 2009; Moss et al., 2012a)?

To date, shrimp breeders have focused most effort on developing families of

shrimp with resistance to Taura syndrome virus (TSV) and White spot syndrome

virus (WSSV) (Argue et al., 2002; Cuéllar-Anjel et al., 2011; Cuéllar-Anjel et al.,

2012; Gitterle et al., 2005a; Huang et al., 2012; Jiang et al., 2004; Kong et al., 2003).

TSV is a single stranded RNA virus that belongs to the family Dicistroviridae

(Bonami et al., 1997). The first record of TSV epizootic was in the mid-1990s in

Ecuador but TSV quickly spread throughout the Americas (Lightner et al., 2009b).

Later in 1993, this virus spread to the shrimp farming in Asia (Phalitakul et al., 2006;

Tu et al., 1999). TSV can infect shrimp at early nursery stages or in pond growout at

2-4 weeks, and can cause highly cumulative mortality reaching as high as 80-90%


(Brock, 1997; Lightner, 2003b). In 1993, the revenue losses caused by TSV were

estimated at 400 million USD in Ecuador alone (Lightner, 1999).

WSSV in contrast, is a double stranded DNA virus that belongs to the family

Nimaviridae (Escobedo‐Bonilla et al., 2008). This virus was first recorded in Taiwan

in 1992 (Chou et al., 1995), and rapidly spread throughout Asia (Flegel and

Alday‐Sanz, 1998; Mohan et al., 1998; Park et al., 1998). In 1995, this virus was first

identified in the US, and appeared in other shrimp farming regions in America by

1999 (Lightner, 2011; Lightner, 1996). In November 2016, WSSV was identified in

a black tiger shrimp farm near Brisbane (Queensland, Australia), and quickly spread

to other shrimp farms by February 2017 (Knibb et al., 2018; Oakey and Smith, 2018).

As with TSV, WSSV can cause serious economic losses (Lightner, 2003a) with

WSSV-infected shrimp ponds suffering cumulative mortality of more than 90% over

3-10 days (Lightner, 1999; Wang et al., 1999). Perhaps due to very different

magnitudes in estimated genetic parameters for resistance, selection results for

controlling the two viruses have also varied widely.

Overall, genetic parameter estimates for penaeid TSV resistance are moderate (0.2

≤ h2 ≤ 0.3) (Argue et al., 2002; Cock et al., 2009; Fjalestad et al., 1997). Despite

only relatively moderate heritability for TSV resistance, TSV resistant strains have

been developed. Argue et al. (2002) reported that survival from TSV infection

increased 18.4% for selected families of P. vannamei compared with a control line

after a single generation of selection. Over a three year selection program, mean

survival after TSV exposure increased 24% to 37% in the selected line of P.

vannamei (White et al., 2002). After 15 generations of selection, researchers at the

Oceanic Institute (Hawaii, USA) reported several families showing 100% survival

after TSV exposure (Moss et al., 2011). Now, TSV-resistant broodstock are widely


used in commercial hatcheries and TSV is no longer considered a major threat to the

global shrimp farming industry (Moss et al., 2012a).

In contrast, published heritability estimates for WSSV resistance are generally

very low (h2 range from 0.00 to 0.21) with most estimates below 0.1 (Gitterle et al.,

2006a; Gitterle et al., 2005a; Gitterle et al., 2006b). This means that selection for

WSSV resistance is problematic due to very low additive genetic variance. For

instance, Gitterle et al. (2005) reported only a 2.8% improvement in survival rate

after a single generation of selection. In contrast however, Huang et al. (2012)

reported producing families of P. vannamei with 25.33% to 82.14% higher survival

than unselected shrimps in a commercial culture environment, while Cuellar-Anjel et

al. (2011) reported producing families of P. vannamei that showed survival rates

ranging from 23% to 57% after WSSV exposure. Currently however, there is no

breeding program anywhere in the world that provides reliable WSSV resistant stock

to the commercial shrimp industry.

1.4.4.5 Genetic Parameter Estimates for Reproductive Traits

If we divide the users benefiting from genetic breeding goals into three major

groups, they include: shrimp farmers, hatcheries and post-larvae nurseries. The

breeding traits identified earlier (fast growth, improved pond survival and disease

resistance), are all traits important for shrimp growout farms. Hatcheries and

nurseries in contrast, want strains that show high fecundity, more frequent spawning

by ablated females, a high ratio of mating success, high incubation rate of eggs, and

better survival rate from nauplii to post larval stage. These traits together can be

described as reproductive traits. To date, most breeding programs for shrimp have

paid more attention to fast growth, high survival rate and disease resistance traits

(Benzie, 2009; Gjedrem and Rye, 2016), with no published studies available on


reproductive traits as defined breeding goals for penaeid shrimps, although

reproductive characters are essential to the viability of both hatcheries and post-

larvae nurseries (Arcos et al., 2004).

Time of first spawning after ablation shows the highest heritability among

reproductive traits, with estimates ranging from 0.41 to 0.47 (Arcos et al., 2004;

Macbeth et al., 2007). Four studies have estimated the numbers of eggs (NE) per

spawning event as a trait, with the heritability estimate quite high, suggesting

promise if a family-based selection program was implemented. h2 estimates ranged

from 0.09 to 0.41, with an average of 0.2 (Arcos et al., 2004; Caballero-Zamora et al.,

2015a; Macbeth et al., 2007). Spawn frequency is also a crucial reproductive trait. In

commercial shrimp hatcheries, a large percentage of mature females never spawn or

spawn only a few times while only a relatively small percentage of females spawn

multiple times, a critical trait that if improved, will increase total nauplii production

(Ibarra et al., 2007b; Ibarra et al., 2005). Therefore, a focus on this trait could

significantly improve nauplii production and save a large proportion of the cost of

maintaining mature females that never spawn or spawn only a limited number of

times. Ibarra et al. 2005 showed that “number of spawns” showed moderate h2 (0.20)

which could potentially underpin improvement via family selection enhancing total

nauplii production per female. Conversely, the number of nauplii produced per

female showed very little additive genetic variation and heritability for this trait

ranged only from 0.03 to 0.07 (Caballero-Zamora et al., 2015a; Macbeth et al., 2007).

Finally, h2 estimates for other reproductive traits including egg chemical composition,

egg diameter, ovary maturity etc. are also of interest (Arcos et al., 2004; Ibarra et al.,

2007b). In the future, more studies of these novel traits will be required to allow a


more complete and comprehensive approach to stock improvement of the target

species to be undertaken.

1.4.4.6 Additional Traits Identified for Genetic Improvement in Penaeid Shrimp

In breeding programs for aquatic species, with the exception of selecting for

growth rate, survival rate, disease resistance, and reproductive traits, a number of

other commercially important traits have been identified as targets for breeding goals,

including: meat quality, external pigmentation, special environmental tolerance,

frequency of deformity, feed conversion efficiency, and uniformity in body weight

(Gjedrem and Baranski, 2010; Gjedrem and Rye, 2016; Sae‐Lim et al., 2016).

Among these, uniformity in body harvest weight is considered to be a major target

(Khaw et al., 2016; Sae‐Lim et al., 2016). Improving levels of uniformity could

potentially reduce competition among growout animals and increase their

commercial value at harvest that would benefit both retailers and food processors

(Khaw et al., 2016).

In Nile tilapia, heritability for uniformity at harvest and correlations with harvest

weight have been estimated at 0.23 and 0.17, respectively (Khaw et al., 2016). This

implies that it should be possible to improve uniformity at harvest by family

selection and that high growth rate and better uniformity can be targeted,

simultaneously. Genetic heterogeneity in harvest weight has been demonstrated

experimentally and it has been evaluated in some livestock species but has yet to be

evaluated in shrimp (Garreau et al., 2008; Gutiérrez et al., 2006; Rönnegård et al.,

2013; Vandenplas et al., 2013). It would be interesting therefore, to determine if it is

possible to combine selection for fast growth rate with better uniformity at harvest

simultaneously in farmed shrimp species.


1.4.5 Genotype-by-Environment (G×E) Interactions

Ideally, improved seed developed in breeding programs are also more productive

under a range of different commercial culture environments (Sae‐Lim et al., 2016).

Relative performance of a specific animal phenotype will depend however, on both

their individual genotype, the production environment to which they are raised and

the interaction between these factors. G×E interactions are a phenomenon where the

same genotypes can produce different phenotypic responses under different

environmental conditions (Falconer et al., 1996; Lynch and Walsh, 1998; Sae‐Lim et

al., 2016). It is essential therefore to assess G×E interactions to determine if an

improved animal will perform equally well in different production environments.

Studies of G×E interactions in penaeid shrimp species have focussed on correlations

between specific growth traits and different culture environments, in particular,

effects of stocking density, location and temperature (Caballero-Zamora et al., 2015b;

Campos-Montes et al., 2009; Castillo-Juárez et al., 2007; Coman et al., 2004;

Fjalestad et al., 1997; Gitterle et al., 2005a; Ibarra and Famula, 2008a; Jerry et al.,

2006b; Li et al., 2015; Pérez‐Rostro and Ibarra, 2003a; Suarez et al., 1999; Sui et al.,

2016c).

Perez-Rostro and Ibarra (2003) reported no differences in family breeding value

ranks in P. vannamei for body weight at 200 days testing when stocks were grown at

two densities (2.5 and 4.3 individuals/m2). Gitterle et al. (2005) also did not detect

any significant G×E interactions in P. vannamei for body weight at 160 days under

seven different commercial shrimp farming environments where stock density and

salinity level varied. Similarly, Castillo-Juarez et al. (2007) found high genetic


correlations (0.80-0.86) in P. vannamei for body weight after 130 days at two

different locations with stocking densities of 9 and 14 individuals/m2. Campos-

Montes et al. (2009) did not observe evidence for significant G×E interactions in P.

vannamei for body weight at 130 days under three culture densities (10, 30 and 85

shrimp/m2). In contrast, genetic parameters for P. vannamei can vary significantly

under some suboptimal environmental conditions including low temperature (Li et al.,

2015) and the presence of a natural WSSV outbreak (Caballero-Zamora, Montaldo et

al., 2015).

In Asia, including in China which is the largest P. vannamei producer, extremely

diverse farming conditions exist due to production across a wide geographical range

from the tropics to temperate climates, the use of different culture densities, pond

types, management practices, etc. Significantly, farming P. vannamei in freshwater

contributes equally with marine culture in China. Evaluating growth traits for G×E

interactions between freshwater and marine culture environments will be essential to

developing a sustainable shrimp industry in Asia in the future.

1.5 Bridging the Gap between Population Genetics/Genomics and

Quantitative Genetics

1.5.1 Molecular Markers

The fundamental tool for production improvement of penaeid shrimp is based on

quantitative genetics. Though most principles have been developed over the last one

hundred years (Hill, 2014), our understanding of the nature of quantitative traits is

still rudimentary (Hill, 2010). For example, how many genes affect a specific trait

and how they interact, how their levels affect layering, their individual relationships

to overall fitness, and why it is important to maintain variation (Hill, 2010). We can

achieve significant genetic gains, but this process is largely achieved via a ‘black


box’ approach that essentially ignores the molecular level. Development and

application of molecular marker technologies via population genetic and genomic

approaches have assisted stock improvement programs. The primary molecular

markers of interest in genetic improvement of penaeid shrimps have been PCR-based

techniques that include; microsatellites (SSRs), single nucleotide polymorphisms

(SNPs), and mitochondrial DNA markers (mtDNA) (Alfaro-Montoya et al., 2018;

Guppy et al., 2018). While application of these markers do not require highly trained

skilled staff, costly lab facilities and sophisticated data analysis and interpretation,

they have been applied successfully to characterise genetic diversity, and population

structure of wild genetic resources of P. vannamei (Mendoza-Cano et al., 2013;

Valles-Jimenez et al., 2004), P. monodon (Abdul‐Aziz et al., 2015; Alam et al., 2016;

Benzie et al., 2002; Walther et al., 2011; Waqairatu et al., 2012; You et al., 2008), P.

japonicus (Tsoi et al., 2005; Tsoi et al., 2007; Tsoi et al., 2014), P. merguiensis

(Wanna et al., 2004), P. indicus (Alam et al., 2014; De Croos and Pálsson, 2010),

and P. semisulcatus (Alam et al., 2017). In addition, molecular marker technologies

have also facilitated broodstock management of captive penaeid stocks (Goyard et al.,

2003; Knibb et al., 2014; Maggioni et al., 2013; Perez-Enriquez et al., 2009). The

addition of genotypic information and knowledge of levels of genetic diversity can

be used to assess stock differentiation, and genetic relatedness, so that the markers

can be implemented to broaden genetic variation in base lines for genetic

improvement programs using domesticated strains (Ren et al., 2018). Compared

however, with research in the poultry industry and on other domesticated terrestrial

farm animal strains, application of molecular markers to improving production of

peneaid shrimps still lags far behind. We still do not have public widely available

marker panels, therefore results achieved in different labs or in different countries


cannot be compared or shared easily. Moreover, for farmed penaeids at least,

knowledge about genetic variation generated from neutral molecular markers to date,

cannot be linked directly to genetic variation in quantitative traits using a

conventional selective breeding approach. However, pedigree information based on

molecular markers (SSRs or SNPs) via parentage assignment have recently offered

new ways to exploit genetic variation of quantitative traits in farmed shrimp.

1.5.2 Parentage Assignment

Parentage assignment is based on SSR or SNP markers for family pedigree

identification systems (Jones et al., 2010). Basically, there are two computational

methods for pedigree reconstruction: exclusion methods and likelihood methods

(Vandeputte et al., 2011). The exclusion approach makes no hypotheses other than

Mendelian segregation of alleles, is very simple and has been applied effectively in

genetic improvement of aquaculture species via software programs such as

PROBMAX (Danzmann, 1997), VITASSIGN (Vandeputte et al., 2006), and FAT

(Taggart, 2007). In contrast, likelihood methods are based on probability of allele

frequencies, and likelihood software programs are now available for aquaculture

species and include CERVUS (Kalinowski et al., 2007), PAPA (Duchesne et al.,

2002), and PARENTE (Cercueil et al., 2002). Generally, 8-15 microsatellite markers

can result in an assignment power >99% in aquatic species involving a few tens or

hundreds of parents (Vandeputte and Haffray, 2014). When SNPs are used, it is

estimated that ~6 SNPs can achieve the same assignment power equal to a single

microsatellite (Glaubitz et al., 2003). In experimental research, parentage assignment

panels have been developed for genetic improvement projects in P. vannamei (Harris

et al., 2016; Perez-Enriquez and Max-Aguilar, 2016), P. monodon (Jerry et al., 2006a;

Zhu et al., 2017), P. chinensis (Dong et al., 2006), P. japonicus (Jerry et al., 2004),


and P. merguiensis (Nguyen et al., 2014). Additionally, the potential of parentage

assignment has been demonstrated in a real commercial breeding program for P.

vannamei (Nolasco-Alzaga et al., 2018).

Implementation of parentage assignment in conventional selective breeding

programs can have many benefits for production improvement of penaeid shrimps.

The most important benefit is that it can allow more accurate estimation of genetic

parameters from communally reared families at very early developmental stages

which decreases common environmental effects for estimates of genetic parameters

(Vandeputte and Haffray, 2014). Moreover, it provides an efficient tool for

controlling inbreeding that allows the breeder to exploit a higher selection pressure in

commercial programs, while still controlling inbreeding effectively (Perez-Enriquez

and Max-Aguilar, 2016; Vandeputte and Haffray, 2014). It can also significantly

decrease infrastructure demands in breeding programs including the need for

maintaining families in separate tanks (Yue and Xia, 2014). Future directions for

study of parentage assignment in penaeid shrimps will however, require researchers

to establish widely accepted high quality public panels for target species that enable

comparisons and sharing of results among different labs and countries.

1.5.3 Quantitative Trait Loci (QTL) Mapping

The primary drivers behind integrating population genetics/genomics into

selective breeding is to understand the links between genetic variation and

phenotypic variation in economic traits (Abdelrahman et al., 2017). QTL mapping

using molecular markers can be used to detect the effects of individual functional

genes on a trait and to assist in selective breeding (MAS) to improve commercial

traits (Naish and Hard, 2008). Due to the limited number of markers used and the

relatively small numbers of individuals used in early QTL studies, this approach had


only limited power to accurately isolate QTL effects (Alcivar-Warren et al., 2007;

Du et al., 2010; Wang et al., 2012). Early findings about the inferred roles of specific

genes and genomic regions in these studies still require future validation

(Andriantahina et al., 2013a; Li et al., 2006b; Lyons et al., 2007). While recent QTL

studies of penaeid shrimp stocks have been remarkably improved in terms of marker

density (3, 959 – 4, 626 SNPs), it remains difficult to integrate identified QTLs (Lu

et al., 2016; Robinson et al., 2014; Yu et al., 2015) into industrial-scale genetic

improvement programs. Even after an extensive review of QTL studies conducted on

aquaculture species (more than 40 species examined), only two case studies were

identified that had applied MAS in a genetic improvement program effectively (Yue,

2014). Over the longer term, genomic selection (GS) which applies large numbers of

SNP markers in a panel is likely to be a better option than approaches used in the

past (Castillo-Juarez et al., 2015).

1.5.4 Genomic Selection

Genomic selection (GS) is a relatively new approach for selective improvement of

quantitative traits and is based on high density molecular marker panels distributed

over the whole genome. This method integrates marker data with phenotypic and

pedigree data that can improve selection outcomes compared with conventional

selective breeding. In this context, the link between population genetics/genomics

and quantitative genetics is tighter than ever before with respect to improving

domesticated animals (Hill, 2014). A landmark article on GS was published in 2001

(Meuwissen et al., 2001) that provided the first statistical model directed at using

high-density genomic data to increase accuracy of selection. Subsequent modelling

published on GS has had a large effect accelerating progress in this field (Schaeffer,

2006), and this approach has been applied rapidly to commercial improvement of


livestock species (Hayes et al., 2009). In Dairy Science, GS has almost replaced the

conventional breeding approach of progeny testing and produced significant

increases in rate of genetic gains achieved while shortening the generation interval

between selection events (Hickey et al., 2017a). The transition to GS for stock

improvement has enabled an almost doubling of genetic gains in milk yield in the

USA (Wiggans et al., 2017). Application of GS to genetic improvement of penaeid

shrimps however, lags behind developments in terrestrial livestock research, due to

both the shortage of genome information on aquatic species and the high cost of

genotype sequencing. The prerequisite for successful application of GS to a new

target species is the power density (>10k) of commercial SNPs chips that are

available. Most commercial genotyping arrays used for GS in livestock species

contain 50 k ~ 500 k SNP markers (Kranis et al., 2013; Matukumalli et al., 2009;

Wiggans et al., 2017). In penaeid shrimps, there have been two commercial SNPs

arrays developed, one for P. monodon (Baranski et al., 2014) and another for P.

vannamei (Jones et al., 2017), but both are less than 10 k in size and hence may not

be adequate for GS in a commercial project. Moreover, individual genotype

sequencing costs remain expensive, and this alone may restrict future application of

GS to genetic improvement of penaeid shrimps. Of equal importance is availability

of a high quality draft genome assembly that is fundamental for the development of

high density SNP arrays. The first draft of a penaeid shrimp genome for P. vannamei

has only just been released publicly (Zhang et al., 2019).

1.5.5 Whole Genome Sequencing of Aquatic Species

Information on whole genome architecture provides a fundamental tool for

assisting improvement of aquatic species in breeding programs. In particular, this

cutting-edge genetic technology can be applied in aquaculture to; develop a reference


genome, identify SNPs for genome-wide association studies (GWAS) and to apply

genomic selection (GS), to characterise the roles of functional genes linked to

commercially important traits, and to address specific biological questions (Guppy et

al., 2018; Yue and Wang, 2017). Since the emergence of NGS technology in 2005,

aquaculture genome sequencing projects have expanded rapidly in particular, in the

USA, China and the EU but also elsewhere. Different sequencing strategies and

different assembly pipelines have been used already to sequence the complete

genomes of a small number of farmed aquatic species. While a cost-effective fosmid-

pooling strategy was used for genome sequencing in the Pacific oyster (Crassostrea

gigas) (Zhang et al., 2012), a whole-genome shotgun strategy was used for whole

genome sequencing of common carp (Cyprinus carpio) (Xu et al., 2014). Currently,

short-read sequencing in combination with extremely long fragment sequencing (e.g.

SMRT sequencing) is the most widely-used method applied in aquaculture genome

projects (Ao et al., 2015; Roberts et al., 2013; Vij et al., 2016). To date, whole

genome sequences from 24 farmed aquatic species have been published (Yue and

Wang, 2017). For shrimp species however, the first assembled genome sequence for

P. vannamei was published recently based on a sequence assembly from a single

adult male and covered only ~1.66 Gb of the whole genome (Zhang et al., 2019).

Incomplete sequences are an issue with shrimp genomes because they are in general,

comparatively large and often contain high numbers of repeat sequence regions that

make genome sequencing problematic (Guppy et al., 2018; Huang et al., 2011).

Since accuracy problems with SMRT sequencing (PacBio) have now largely been

resolved, this major issue is likely to be addressed in the near future. A high quality

draft genome for P. vannamei would deliver many benefits to the shrimp farming

industry. It will not only unleash the genetic potential for new breed improvement


projects, but will also allow a better understanding of key functional traits to be

developed, including for osmoregulation and sex determination traits, while

improving our understanding of the genetic architecture of the complex shrimp

immune defence system. This is a key issue related to addressing the major disease

problems common in shrimp farming, worldwide.

1.6 Issues with P. vannamei Broodstock Quality in China

1.6.1 Limitation of the Imported SPF Broodstock

While the Asia-Pacific is the dominant region for shrimp production worldwide

and currently contributes more than 90% to total world production (FAO, 2016a; b),

almost all of the genetically improved broodstock in this region come from SPF lines

imported from the Western Hemisphere. Annual demand for SPF broodstock is

approximately 1 million P. vannamei pairs (Gitterle and Diener, 2014), of which,

nearly 400,000 pairs were marketed to China. While imported SPF seed show

improved growth performance, they also have low survival rates and high disease

susceptibility both serious problems that have developed with use of SPF seed in

Asia (Cock et al., 2017; Cock et al., 2009). Most SPF stocks purchased from the

USA are susceptible to many novel Asian pathogens and diseases, and this problem

has also become an issue in the South American shrimp industry (Cock et al., 2017;

Moss et al., 2012b). To develop SPF stock, larvae, juveniles, and pre-mature adults

are cultured under bio-secure conditions (Briggs, 2005; Lightner, 2003a) and genetic

parameters for selecting traits in experimental environments are likely to be very

different in regions where little attention is paid to biosecurity, in particular, in the

shrimp farming industry in the Asia-Pacific region. This would be particularly true

for traits related to pond survival and disease resistance that are significantly


impacted by G×E interactions (Cock et al., 2017; Li et al., 2015; Sae‐Lim et al.,

2016). This implies that in general, current SPF breeding goals are unlikely to be

appropriate for non-biosecure shrimp culture environments (Cock et al., 2017; Cock

et al., 2009). The main reason why classical SPF breeding does not test breeding data

based on real commercial pond systems is because of limitations on pedigree

recording methods that require elastomer (VIE) tagging or genetic markers (SNPs or

SSR) to be used for parentage assignment, and these technologies are both expensive

and labour intensive. In the future, new approaches to family selection in penaeid

shrimp that test full and half sibs under commercial production conditions may

address poor survival of SPF lines in many regional industries.

1.6.2 Inbreeding

There are several reasons why in general, inbreeding levels in P. vannamei

broodstock are relatively high in Asian shrimp culture. Major reasons include;

shortage of supply of genetically improved SPF broodstock resulting in small

entrepreneurs using second generation parents sourced directly from their own or

local culture ponds without applying any controls on inbreeding level (Doyle, 2016;

Thitamadee et al., 2016). For instance, broodstock demand in China is approximately

2.5-3 million pairs annually but imported SPF parents contribute only 400,000 pairs

(pers. comm.). This problem also affects Thailand, Ecuador and a number of other

developing countries (FAO, 2016b). Inbreeding problems are also impacted by the

actions of SPF line breeders because in order to protect their investment, SPF

suppliers have a tendency to export broodstock from only a single line and

unauthorized breeders in developing countries then use stock from the next

generation as parents. This practice can result in high inbreeding rates with

consequential potential for inbreeding depression to develop over time (Doyle, 2016;


Moss et al., 2007). But more importantly, specific reproductive traits, the high

fecundity of penaeid shrimp and a small proportion of females that spawn multiple

times all contribute to the majority of progeny produced (Ibarra et al., 2007b; Ibarra

et al., 2005). Together these factors can significantly increase inbreeding risk in a

captive (closed) population. The only real solution to this problem will be for

regional shrimp producers to initiate their own local breeding programs and provide

better support for both local hatcheries and shrimp farms.

1.6.3 Issues of Base Population

Decisions about different approaches for producing the best base population can

be critical for success of any stock improvement program (Fernandez et al., 2014;

Holtsmark et al., 2008a; b). Essentially, long-term success of a genetic breeding

program will be contingent on how much broad additive genetic variance is captured

in the foundation base population (Loughnan et al., 2016). A fundamental obstacle

for initiating a genetic breeding program for P. vannamei in Asia is how to collect an

appropriate level of genetic variation in the base population because Pacific white

shrimp is an exotic species to this region (FAO, 2016b).

To date, there have been very few studies that have applied genetic theory and

molecular data directly to capture broad genetic variation in founding broodstock

populations of target aquatic species (Hayes et al., 2006). In fact, the majority of

breeding programs for aquatic species have essentially built founding populations

based largely on the stock they had available at the time. A simulation study has

suggested that assuming individuals for a founding population are genetically

unrelated, and that similar levels of genetic variation are present among

subpopulations, optimal levels of genetic variation can be achieved by choosing four

or more discrete subpopulations and cross mating them to produce a diverse base


population (Holtsmark et al., 2006; Holtsmark et al., 2008a; b). A modelling case

study also showed that computer simulations that combine genome-wide DNA

marker information and phenotypic values from selected broodstock can maximize

genetic variation (6% higher) than sampling equal numbers from each contributing

strain (Fernandez et al., 2014). No published studies of real aquaculture breeding

programs up to now have applied or tested the above ideas.

Neutral molecular markers (e.g. microsatellites) can provide information about

relative levels of genetic diversity, population structure, stock relatedness and

kinship in both cost and time effective ways. This technology has been applied

successfully in stock conservation and management of breeding programs in a

number of terrestrial domesticated farm animals (Carvalho et al., 2015; Revidatti et

al., 2014; Wilkinson et al., 2011; Wilkinson et al., 2012). For aquatic farm species

however, to date there are only a relatively few examples where genotype

information on domesticated strains has been included in genetic improvement

programs (FAO, 2011).

1.7 Aims of the Current Project

Pacific white shrimp have become the pre-eminent farmed aquatic species and a

major food commodity in terms of trade value in world aquaculture. As this change

has occurred, China at the same time has become the world’s largest farmed shrimp

producer. Sustainability of the farmed shrimp industry in China however, faces

significant challenges; in particular, pond survival rates of farm strains are generally

very low. This problem offers an opportunity for industries to design better breeding

programs and to develop locally adapted strains that show high survival and

improved growth rates that will generate greater profits while targeting the specific

farm and market conditions present in China. Thus, the main objective of the current


project was to develop a high performing locally adapted culture strain for the shrimp

farming industry in China.

1.8 Objectives and Thesis Outline

1.8.1 Chapter (1): General Introduction

Chapter (1) is an introduction that illustrates the background and rationale for the

current project via a literature review approach. It also outlines the gaps in

knowledge that the project seeks to address.

1.8.2 Chapter (2): Characterization for the Culture Resources of Pacific White

Shrimp for Genetic Diversity, Genetic Structure, and Genetic Relatedness

Chapter (2) investigates the origins and relative levels of genetic variation in

domesticated strains in China, developing an argument for development of a broad

synthetic base population for a genetic improvement project there. This chapter

forms an article published recently in the journal Aquaculture (Aquaculture, (2018),

491, 221-231). Material presented here provides the most comprehensive analysis of

the status of genetic diversity levels and inferred genetic origins of farmed Pacific

white shrimp broodstock in China. In addition, the section combines information on

historical origins of Pacific white shrimp in China and extent of genetic

differentiation among 36 breeding lines sourced there for this study.

1.8.3 Chapter (3): Genetic Parameters for Body Weight and Survival in the Base

Population

Chapter (3) examines how much additive genetic variation is available for

production of a base population via applying genotypic information. Quantitative

data on 89 families were used in the analysis. This chapter is the first report of a


penaeid shrimp breeding program that employed domesticated strains based on an

experimental design that applies a “genotype approach” to initiate a genetic selection

program. This chapter forms an article currently ‘under review’ in the journal -

Aquaculture.

1.8.4 Chapter (4): Comparison of Reproductive Performance of Female Pacific

White Shrimp Reared in Recirculating Tanks vs Earthen Ponds

Chapter (4) examines the two candidate production environments (RT vs EP) used

for rearing Pacific white shrimp in China and assesses in which system cultured

females show better reproductive performance. This chapter presents the first

analysis of several key reproductive traits in mature females, in a scenario that

impacts commercial nauplii production based on a natural mating design system.

Data generated in this study will be essential for improving management of Pacific

white shrimp in a genetic improvement program to optimise reproductive

performance of commercial broodstock, to improve husbandry practices, and will

assist in developing an effective strategy for future seed dissemination of improved

broodstock. This chapter forms an article currently being prepared for the journal

‘Aquaculture’.

1.8.5 Chapter (5): Quantitative Genetic Analysis of Female Reproductive Traits

Chapter (5) investigates the levels of additive genetic variation for reproductive

traits in females that are considered among the top commercial factors that impact

successful nauplii production in hatcheries in China and addresses the specific

question ‘can we improve female reproductive traits via genetic selection. This

chapter also examines genetic correlations between body weight and certain


reproductive traits after spawning, to answer the scientific question: ‘does selecting

for improved body weight in females produce any potentially negative effects on

broodstock reproductive quality?’ This chapter forms an article currently being

prepared for the journal ‘Aquaculture’.

1.8.6 Chapter (6): General Discussion

Chapter (6) summarises and integrates results from each experimental chapter to

address the overall objective of how in combination, they can contribute to genetic

improvement of Pacific white shrimp in China. The section also provides final

conclusions and recommendations for future research to assist meeting the overall

project goals.

43 Chapter 2: Levels of Genetic Diversity and Inferred Origins of Penaeus vannamei Culture Resources inChina: Implications for the Production of a Broad Synthetic Base Population for Genetic Improvement

Chapter 2: Levels of Genetic Diversity and Inferred Origins of

Penaeus vannamei Culture Resources in China: Implications for the

Production of a Broad Synthetic Base Population for Genetic Improvement

This Chapter has been published in Aquaculture:

Ren, S., Mather, P. B., Tang, B., & Hurwood, D. A. (2018). Levels of genetic diversity and

inferred origins of Penaeus vannamei culture resources in China: Implications for the

production of a broad synthetic base population for genetic improvement. Aquaculture, 491,

221-231.


ABSTRACT

Knowledge about the origins and relative levels of genetic variation available in

domesticated strains can be used to develop robust synthetic populations for use in stock

improvement programs, in particular, where wild stocks are unavailable or the target species

is alien. Here, we undertook a case study of Pacific white shrimp (Penaeus vannamei) where

we utilised neutral molecular genetic variation data, population structure, and genetic

relatedness as an initial step towards producing a base line for a long-term family-selection

breeding program in China. The objective was to capture and maximise genetic variation in

the base population while at the same-time controlling inbreeding levels. Genetic diversity in

1162 individuals from 36 breeding lines sourced from 22 hatcheries with domesticated

broodstock available in China were assessed using seven microsatellite loci. Genetic diversity

levels in the sampled lines were considered to be healthy, with a mean number of alleles per

locus (A) 5.85, ranging from 4.00 in B4 to 12.43 in T1, and mean allelic richness (Ar) 4.46,

ranging from 3.35 in B4 to 6.69 in T1 breeds. Mean inbreeding coefficient (Fis) was 0.07

among the 36 lines indicating acceptable levels of inbreeding. Genetic differentiation

between lines from different hatcheries was moderate, with a mean FST estimate of 0.09

among all pairs of hatchery lines. Bayesian assignment and phylogenetic analyses indicated

that hatchery lines in China could be broadly divided into four groups: i) a line from North

America (NA1), originally sourced from Mexico (north Sinaloa) and Ecuador, ii) two

representatives of the Kona line developed in Hawaii (Oceanic Institute) (NA2,3), iii) a line

developed in Thailand (Southeast Asia 1 (SA1)), and iv) a mixed group containing stocks that

originated in Latin America (predominantly from Panama and Colombia) (LA; SA2; CN1,2;

NA4,5). From these data, we argue that a complete 4×4 diallel cross (16 crosses) of the four

subpopulations should be trialled in China to develop a base line for a family selection


breeding program with optimal levels of genetic variation and a relatively low inbreeding

coefficient.

Keywords: Microsatellite, genetic diversity, population structure, prawn aquaculture,

Bayesian assignment


2.1 INTRODUCTION

Capturing high levels of genetic variation in a base population in stock improvement

programs is crucial for long-term success (Holtsmark et al., 2006; Holtsmark et al., 2008a; b).

Achieving this however, can be problematical in places where the culture species is not

native and it is difficult to source wild germplasm. High levels of genetic variation is

important because genetic gains, when selecting for specific traits, will depend on the levels

of additive genetic variance present in the base population, the selection intensity applied and

the relative level of heritability of the target traits (Falconer and Mackay, 1996; Gjedrem and

Robinson, 2014). A number of well-organized breeding programs for aquatic species have

ultimately failed because exploitable levels of genetic variation in the base population were

low (Huang and Liao, 1990; Teichert-Coddington and Smitherman, 1988). In contrast,

lessons learned in two well-documented highly successful aquatic breeding programs that

provide models for other species i.e. Atlantic salmon in Norway (Gjedrem et al., 1991) and

the Genetic Improvement of Farmed Tilapia (GIFT) (Eknath et al., 1998) in Asia included

both wild and domesticated strains when base populations were established. The importance

of combining wild populations and ‘domestic’ strains contributing to a highly diverse base

population is clear in the GIFT case where change in growth rate was impressive with an

average genetic gain of 10% per generation when compared with a control line (Hamzah et

al., 2014; Nguyen, 2016). Estimates of heritability for body weight (h2=0.28) in the latest data

(year of 2012) indicate that exploitable genetic variation remains high in GIFT strains so

genetic gains from the family selection approach should be ongoing (Nguyen, 2016). In

general, results from incorporating domestic strains in aquatic animal stock improvement

programs suggest that this approach is productive and can address shortages of high quality

broodstock in non-native finfish aquaculture. Currently, farming of exotic aquatic species

accounts for a significant component of aquaculture across the world and has contributed


significant economic and great societal benefits, in particular in the Asia-Pacific region (Silva

et al., 2009; Singh and Lakra, 2011).

There can be a number of advantages associated with using domesticated strains to

produce a base population in selective breeding programs of alien species. For example,

much of the worldwide production of farmed Pacific white shrimp (Penaeus vannamei) and

tilapia occurs in areas outside the natural geographic ranges of both species that make

availability of wild strains challenging due to geographical distance, quarantine requirements

for introductions and costs associated with sampling and transportation. Local domestic

strains, where available, can provide a much easier and lower cost option for use in stock

improvement programs. Secondly, domesticated strains have often accumulated favorable

traits for artificial culture environments over many years that make them easier to handle and

to breed compared with wild strains (Olesen et al., 2015). When developing a base population,

use of domestic or genetically improved strains, rather than wild animals as the starting

resource, can provide the new breeding line with a competitive start, in particular for growth

performance (Fernández de Alaiza García Madrigal et al., 2018).

Farmed shrimp is a major commodity in the world seafood market, with annual production

of more than 4.3 million tonnes worth more than $USD 22 billion (FAO, 2016c; Fernández

de Alaiza García Madrigal et al., 2018). Pacific white shrimp (Penaeus vannamei) that is

native to southern North America, Central America and northern South America, now plays a

substantial role in shrimp farming worldwide. In the Asia-Pacific region (where

approximately 87% of global farmed shrimp is now produced), this exotic species contributed

78% to total production in 2014, with a biomass of 3.0 million tonnes. This represents the

largest relocation of a single species in the planet’s history (Saumena, 2015; Walker and

Mohan, 2009). Pacific shrimp farming in Asia is now threatened however, by the quality and


availability of seed for the industry. Almost all genetically improved broodstock available in

this region come from Specific Pathogen Free (SPF) lines imported from the western

hemisphere; current annual demand for P. vannamei SPF broodstock is approximately 1

million pairs (Giltterle and Diener, 2014). While SPF seed show improved growth rate

performance, relatively low survival rates and high disease susceptibility are serious

problems that have developed in recent times (Cock et al., 2017; Cock et al., 2009). Most

SPF stocks are susceptible to many local pathogens and diseases (e.g. white spot disease

(WSD), early mortality syndrome (EMS)) (Thitamadee et al., 2016), and this problem has

become a significant issue for the industry, particularly for the Asia-Pacific shrimp culture

industry (Cock et al., 2017; Moss et al., 2012a). One way to address these issues is to develop

locally adapted breeding lines that can be subjected to selection for disease resistance,

improved growth rate, etc. The first step in setting up these lines is to survey available genetic

variation (GV).

Molecular markers (e.g. microsatellites) can provide effective tools for evaluating neutral

genetic diversity levels, population structure and relatedness and kinship in domestic animal

stocks. These characteristics have implications for both domesticated stock conservation and

management of breeding programs (Carvalho et al., 2015; Glowatzki‐Mullis et al., 2009;

Loughnan et al., 2016; Revidatti et al., 2014; Wilkinson et al., 2011; Wilkinson et al., 2012).

Applying information about relatedness among broodstock in artificial mating designs for

stock improvement can provide an empirically based solution to reducing inbreeding levels

when the initial generation is developed (Porta et al., 2006; Rodzen et al., 2004; Sekino et al.,

2004). This approach is highly relevant for genetic improvement in aquaculture, particularly

when alien aquatic species are targeted. This is because there can be critical issues associated

with high inbreeding levels in alien species due to difficulties associated with sourcing new

and genetically diverse broodstock. But more importantly, the high fecundity inherent in


many cultured aquatic animals means that only a few broodstock are required to produce

sufficient offspring to meet demand. The issues of inbreeding are twofold: i) inbreeding can

lead to a decrease in fitness (inbreeding depression) through a reduction in heterozygosity

and an associated increase in the expression of recessive deleterious alleles, and ii) low levels

of genetic diversity can result in a poor response to selection (Li et al., 2004; Schwartz and

Beheregaray, 2008).

Despite the important role of P. vannamei in the shrimp farming industry, studies on

genetic background of both global domesticated broodstock and wild populations are lacking

or have only been limited in scope (Maggioni et al., 2013; Mendoza-Cano et al., 2013; Perez-

Enriquez et al., 2009; Valles-Jimenez et al., 2004; Zhang et al., 2014). The aim of the current

study therefore, was to characterise genetic diversity levels and relationships among

domesticated lines of the exotic farmed penaeid (P. vannamei) in China based on

microsatellite DNA markers. The data generated will be used to inform decisions about the

best approach to establishing a founding stock to capture the highest levels of genetic

variation possible while controlling for inbreeding level from domesticated lines currently

available in country.

2.2 MATERIALS AND METHODS

2.2.1 Sampling

Twenty-two commercial P. vannamei hatcheries from three provinces in southern China

(Fujian, Guangzhou, and Hainan) were sampled to assess levels of genetic diversity,

population structure and relatedness (Table 2.1). Standards for inclusion of specific

hatcheries were as follows: (1) sampling from at least 10 hatcheries that were ranked among

the top 20 performing P. vannamei hatcheries determined by the China Aquatic Products


Processing and Marketing Association (CAPPMA) and where possible, (2) to sample

broodstock with known different origins. The 22 hatcheries are identified but referred to

anonymously as A to V (Table 2.1) to ensure that agreed confidentiality was addressed

appropriately. Provenance of culture lines used to generate the original broodstock in each

hatchery can be attributed to four geographical regions: i) North America (NA), ii) Latin

America (LA), iii) Southern Asia (SA), and iv) China (CN). If multiple breeding lines have

come from the same region, a unique number (1 to 5) was added after the name of the source

region (Table 2.1). Detailed information on line sources are provided on Table 2.1. Up to 50

individuals were sampled per line, with equal contribution by sex. In total, 1162 individuals

from 36 breeding lines from 22 hatcheries were sampled. Pleopods were taken from each

individual and the tissue preserved in 95% ethanol prior to DNA extraction and genotyping.


Table 2.1 Penaeus vannamei sample information.

Yeara Location(Province)

Hatchery PopulationID

Samplesizeb

Source

2013 Hainan A A1 50 NA1c

2013 Fujian B B1 50 NA1c

2013 Guangdong C C1 51 NA1c

2013 Hainan D D1 43 NA1c

2013 Hainan D D2 50 NA1c

2013 Guangdong E E1 50 NA1c


2014 Guangdong E E2 20 NA1c

2014 Hainan F F1 20 NA1c


2014 Fujian G G1 20 NA1c

2015 Hainan B B4 25 NA1c

2015 Hainan H H1 19 NA1c

2013 Guangdong I I1 50 NA2d

2013 Guangdong E E3 50 NA2d

2013 Guangdong J J1 50 NA2d

2014 Fujian K K1 15 NA2d

2014 Fujian L L1 15 NA2d

2014 Guangdong I I2 15 NA2d

2015 Fujian K K2 25 NA2d

2015 Hainan M M1 20 NA2d

2013 Hainan N N1 50 SA1e



2014 Hainan O O1 20 SA1e

2014 Hainan O O2 30 SA1e


2014 Guangdong P P1 30 NA3f

2014 Guangdong P P2 30 NA3f

2014 Fujian Q Q1 30 NA4g

2014 Fujian Q Q2 23 NA4g

2013 Hainan R R1 50 LAh

2013 Hainan S S1 50 CN1i

2013 Fujian T T1 50 CN2j

2015 Fujian U U1 50 SA2k

2015 Hainan V V1 21 NA5l

(a Year of sampling; b Number used for microsatellite markers genotyping; NA1, North America 1; NA2, North America 2; SA1, South Asia

1; NA3, North America 3; NA4, North America 4; LA, Latin America; CN1, China 1; CN2, China 2; SA2, South Asia 2; NA5, North

America 5; c Nucleus line located in USA, source of stock from Mexico and Ecuador; d Nucleus line located in USA, originated form The

Kona line; e Nucleus line located in Thailand, source of stock from USA and South America; fNucleus line located in USA, originated from

The Kona line; g Nucleus line located in USA, originated from a population in Hawaii and a SPR line; h From Colombia; i Developed in

China, source of stock from USA and South America; jDeveloped in China, source of stock from several local populations in China; k

Nucleus line located in Thailand, source of stock from two populations of USA, lNucleus line located in USA, source of stock from two

population from USA).


2.2.2 DNA Extraction and Genotyping

Total genomic DNA was extracted from pleopod samples using a Marine Animal DNA

Extraction Kit (TIANGEN BIOTECH CO. LTD.) following the manufacturers specified

protocols. Seven microsatellite loci developed for the target species: Lvan05 (Perez-Enriquez

et al., 2009), Pvan1758 (Cruz et al., 2002), and TUMXLv5.45, TUMXLv7.121, TUMXLv8.256,

TUMXLv9.103, TUMXLv10.312 (Meehan et al., 2003) were used in the genetic analysis.

Amplification volume was 15 µl with the following conditions: 1×Buffer I (Invitrogen), 0.2

mM dNTP (Invitrogen), 2.5 µM of each primer (forward primers of seven loci were labelled

fluorescently with HEX or FAM), 0.75 U Taq DNA polymerase (Invitrogen) and milliQ

water to a final volume of 15 µl. PCR cycling parameters included initial denaturing step of

95℃, then 35 cycles of 94℃ for 30 s, specific annealing temperature for 45 s, 72℃ for 1 min,

and final extension at 72℃ for 10 min. Genotyping of PCR products was undertaken on an

ABI 3730xl genetic analysis system (Applied Biosystems) and allele size was generated

using GeneMapper 4.0 software (Applied Biosystems) with a GenScan ROX-500 (Applied

Biosystems) internal size standard.

2.2.3 Data Analysis

2.2.3.1 Genetic Diversity Estimates

Number of alleles (A), expected heterozygosity (He), and observed heterozygosity (Ho)

were estimated in GENALEX 6.502 (Peakall and Smouse, 2012). Allelic richness (Ar) and

private allele richness (PAr) were calculated in HP-RARE 1.1 (Kalinowski, 2005), with

parameters set for a minimum of 14 alleles per sample (7 diploid individuals). Ar is a

weighted estimate of the number of alleles at a locus, independent of sample size, by

applying a rarefaction process. PAr is a related estimator of the number of unique or rare

alleles within a stock. Significant differences among genetic diversity measures for each


breeding line were determined using a random complete block ANOVA that recognizes the

inherent lack of independence of each microsatellite locus (block) between strains. Analyses

were performed for mean number of alleles across loci (Ā), and mean allelic richness (Ār –

the number of alleles detected at a locus standardized on the number of individuals genotyped

across all samples). PAr was not tested due to a large zero count. Where significant

differences were detected, a Tukey’s HSD posthoc test was used to identify specific

differences. Inbreeding coefficient (F) was estimated via three methods. Firstly, the widely

used Fis index of the degree of random mating within populations was estimated with

FSTAT 2.9.3.2 (Goudet, 2001) using the Weir and Cockerham method (Weir and Cockerham,

1984). A recent simulation has shown however, that Fis can underestimate real inbreeding

levels for domestic breeding animals compared with a more recent Wang’s TrioML (Ft)

method (Doyle, 2016; Wang, 2014). Thus, a likelihood method that uses genotypes from a

triad of individuals was used to estimate the inbreeding coefficient (Ft) (Wang, 2007; 2011).

Finally, a third inbreeding coefficient index (DyadML (Fd) (Milligan, 2003)) was estimated

to assess the correlation of Fis and Ft values. Deviations from Hardy-Weinberg (HWE) and

Linkage Equilibrium (LE) were assessed using GENEPOP 4.1 (Rousset, 2008), applying a

Markov Chain method with 10,000 dememorization steps followed by 20 batches (100

batches for LE) of 5000 iterations per batch. Sequential Bonferroni was used to adjust

analysis-wide tests for the significance of HWE and LE (Rice, 1989).

2.2.3.2 Population Genetic Differentiation

Genetic differences among 36 stocks from the 22 sampled hatcheries were estimated using

several methods. Firstly, pairwise FST (Weir and Cockerham, 1984), a statistic that partitions

genetic diversity within and among each stock was calculated in ARLEQUIN 3.5.2.2

(Excoffier and Lischer, 2010). Also, a pairwise matrix assessing allele frequency


heterogeneity among samples was constructed using Nei’s DA (Nei et al., 1983), calculated

with Populations 1.2.31 software (Langella, 1999). The PHYLIP program (Felsenstein, 1993)

was used to construct an unrooted neighbour-joining cladogram based on the DA matrix.

Results were entered into FigTree 1.4.3 (Rambaut, 2012) to produce a high-quality

phylogenetic tree representation. Grouping of individuals was performed using a Bayesian

clustering method executed in STRUCTURE 2.3.4 software (Pritchard et al., 2000).

Individuals were assigned to the most probable cluster out of k putative clusters with and

without the use of sample location as a prior reference (‘locprior’). Parameter settings

included admixture and correlated allele frequencies (Falush et al., 2003). For each k (1-35),

20 replicates were run, applying set parameters of 1,000,000 iterations of a Markov Chain

Monte Carlo (MCMC) process with a burn-in length of 100,000 iterations (Gilbert et al.,

2012). Threshold q-value set for stock cluster was >0.9 for a single cluster and <0.9 for the

detection of admixture. The most likely number of genetic populations (k) was estimated with

STRUCTURE HARVESTER (Earl, 2012; Evanno et al., 2005). Admixture proportions of

each stock applying 20 replicates were averaged via CLUMPP 1.1.2 for the best k (Jakobsson

and Rosenberg, 2007) and final barplots were generated with DISTRUCT 1.1 (Rosenberg,

2004).

2.2.3.3 Relatedness Estimates

Estimated relatedness (rxy) among stocks (36 stocks) and average rxy of randomised mating

between populations were estimated using the estimator, rQG (Queller and Goodnight, 1989).

rQG is a widely used index of relatedness used for kinship studies of both captive and natural

populations (Blouin, 2003). In the current study, 1139 individuals (genotyped with a

minimum of four microsatellite loci) were used to generate a relatedness estimates among

stocks. In addition, a moment estimator of pairwise relatedness from LynchRd (rLR) (Lynch

and Ritland, 1999) was calculated for comparison with the rQG method. All relatedness


estimators were generated with COANCESTRY 1.0.1.7 software (Wang, 2011). This

software incorporates seven relatedness and four inbreeding estimators, to generate the most

accurate estimates from multi-locus genotype data. Irrespective of the most appropriate

relatedness estimator, accuracy depends on the dataset in each study; i.e. the number of

microsatellite loci screened and the relative polymorphism information content (PIC) of the

markers employed (Van de Casteele et al., 2001; Wang, 2011).

2.2.3.4 Effective Population Size

Effective population size (Ne1) was estimated using a linkage mating model by assuming

monogamous mating type as reported for both P. vannamei (Harris et al., 2016) and other

closely related penaeid species: P. merguiensis (Knibb et al., 2014) and P. monodon

(Marsden et al., 2013). A linkage mating model Ne1 from 36 hatchery populations was

estimated in LDNE 1.31, applying a minimum allele frequency of 0.05 (Waples and Do,

2008). When considering the impacts of artificial mating design of populations for estimating

Ne, a molecular co-ancestry method (Ne2) was used (Nomura, 2008). Ne2was estimated with

NEESTIMATOR 2.01 that is considered to be a powerful method when there is missing data

as it screens out rare alleles, and it is designed for large data sets (Do et al., 2014).


2.3 RESULTS

2.3.1 Genetic Diversity and HWE Estimates

Mean number of alleles (Ā) per locus in the sampled stocks ranged from 4.00 in B4 to

12.43 in T1, with an overall average across all sampled 36 stocks of 5.85 (Table 2.2). A

similar ranking pattern was observed for allele richness estimates (Ār) compared with Ā, with

a highest value of 6.69 in T1 to the lowest value of 3.35 in B4, and an overall average of 4.46

(Table 2.2). Highest private allele richness (PAr) (0.48) was observed in the V1 stocks,

followed by T1 (0.39) and Q2 (0.28) and stocks from NA1, NA2 and SA1 possessed

significantly lower values of PAr except for the K2 stocks. Expected heterozygosity (He)

across all sampled loci ranged from 0.58 in B4 to 0.81 in T1, while observed heterozygosity

(Ho) ranged from 0.47 in B4 to 0.77 in T1 (Table 2.2). Mean estimates of He and Ho across

all loci and stocks were 0.69 and 0.66, respectively.


Table 2.2 Genetic diversity measures for 36 batches of P. vannamei broodstock (N=1162) from 22 hatcheries inChina based on 7 microsatellite loci: North America (NA), South Asia (SA), Latin America (LA), and China(CN). Sample size (N), average number of alleles (A), allele richness (Ar), private allele richness (PAr),observed heterozygosity (Ho), expected heterozygosity (He), inbreeding coefficient of the Weir and Cockerhammethod (Fis), inbreeding coefficient of Wang’s TrioML method (Ft), inbreeding coefficient of DyadML method(Fd), effective population size of the linkage mating model (Ne1), and effective population size of the molecularco-ancestry method (Ne2).

Source Pop ID N A Ar PAr Ho He Fis Ft Fd Ne1 Ne2

NA1

A1 50 4.00 3.58 0 0.59 0.64 0.10 0.28 0.29 29.4 14.4B1 50 4.57 3.63 0 0.65 0.66 0.02 0.24 0.25 54.4 8.0C1 51 4.71 3.7 0 0.63 0.67 0.07 0.23 0.24 48.9 5.0D1 43 5.00 3.85 0 0.70 0.68 -0.02 0.18 0.19 65.7 32.6D2 50 7.00 4.95 0.01 0.69 0.75 0.09 0.18 0.19 21.9 6.8E1 50 5.00 3.86 0 0.62 0.66 0.08 0.25 0.26 59.9 8.5B2 20 4.71 4.11 0 0.67 0.68 0.04 0.21 0.22 75.4 20.4E2 20 4.57 3.77 0 0.60 0.64 0.08 0.25 0.27 61.2 68.6F1 20 4.57 4.15 0.01 0.69 0.70 0.04 0.15 0.16 Infinite Infinite

B3 15 4.57 4.06 0.08 0.64 0.65 0.07 0.25 0.26 37.7 5.4G1 20 5.43 4.49 0 0.68 0.74 0.10 0.22 0.23 Infinite Infinite

B4 25 4.00 3.35 0 0.47 0.58 0.21 0.41 0.43 749.6 10.4H1 19 4.86 3.92 0.02 0.59 0.62 0.07 0.26 0.28 Infinite Infinite

NA2

I1 50 6.00 4.66 0.01 0.71 0.72 0.02 0.14 0.15 55.8 15.9E3 50 5.86 4.59 0 0.70 0.72 0.05 0.17 0.19 76.5 18.8J1 50 5.29 4.28 0 0.71 0.71 0.02 0.17 0.18 66.3 13.8K1 15 4.57 4.17 0 0.71 0.67 -0.03 0.16 0.17 115.2 7.1L1 15 5.29 4.63 0 0.68 0.71 0.08 0.21 0.22 137.6 Infinite

I2 15 5.43 4.58 0.01 0.67 0.69 0.07 0.19 0.20 51.9 9.3K2 25 6.71 4.5 0.21 0.53 0.61 0.15 0.29 0.31 Infinite 7.8M1 20 4.86 4.11 0.02 0.62 0.66 0.09 0.23 0.24 163.4 26.2

SA1

N1 50 4.71 3.69 0.01 0.62 0.63 0.03 0.26 0.27 96.7 9.3N2 15 5.57 4.51 0.03 0.69 0.69 0.04 0.19 0.20 231.7 30.7N3 20 5.29 4.25 0.06 0.63 0.66 0.07 0.22 0.23 34.6 11.9O1 20 4.57 3.81 0 0.61 0.61 0.03 0.22 0.24 Infinite 33.6O2 30 6.71 4.85 0 0.67 0.73 0.11 0.21 0.23 28.5 6.6N4 20 6.29 4.74 0.05 0.59 0.68 0.16 0.25 0.26 298.9 Infinite

NA3 P1 30 6.00 4.91 0.03 0.72 0.75 0.05 0.17 0.18 53 14.6P2 30 5.57 4.47 0.04 0.71 0.73 0.04 0.18 0.19 73.3 Infinite

NA4 Q1 30 9.00 6.11 0.25 0.71 0.77 0.11 0.21 0.22 134 12.0Q2 23 8.00 5.74 0.28 0.71 0.78 0.11 0.17 0.18 276.3 Infinite

LA R1 50 6.00 4.54 0.23 0.67 0.72 0.08 0.23 0.24 114.1 Infinite

CN1 S1 50 6.43 4.75 0.04 0.64 0.74 0.14 0.23 0.25 92.5 19.2CN2 T1 50 12.4 6.69 0.39 0.77 0.81 0.05 0.13 0.13 361.1 Infinite

SA2 U1 50 10.0 5.67 0.2 0.70 0.75 0.08 0.17 0.18 132.1 20.5NA5 V1 21 6.86 4.81 0.48 0.62 0.69 0.12 0.27 0.28 19.8 5.0Mean --- 5.85 4.46 0.07 0.66 0.69 0.07 0.21 0.22 --- ---


While the inbreeding coefficient estimate (Fis) was low at 0.07 in all stocks, this value

was 3-fold greater where the other two calculation methods were applied, with 0.21 (Ft) and

0.22 (Fd), respectively. The RCB ANOVA identified significant differences among genetic

diversity estimates for the 36 breeding lines (for A, F35, 210 = 7.448, p<0.001; Ar, F35, 210 =

3.738, p<0.001,). In general, the Tukey’s posthoc test demonstrated that breeding lines

derived from CN2, NA4 and SA2 possessed the highest diversity (see Figure 2.1 for A, Ar

(not presented) showed a similar pattern).

Figure 2.1: A bar plot showing mean number of alleles (Ā) for 36 breeding lines. Error bars

represent ±1 SD. Letters above bars indicate where significant differences among breeding

lines exist determined from Tukey’s HSD test (P < 0.05). A1-V1 are 36 breeding lines ID

from 22 hatcheries in China; and NA1-NA5, SA1, SA2, LA, CN1, CN2 are 10 group ID of

the origins history of the above 36 breeding lines (details information see Table 2.1).


Among 252 HWE tests for 7 loci among 36 stocks, only 3 tests showed significant

deviation (P<0.05) from HWE after standard Bonferroni correction. Among these three

significant deviations, there was no obvious pattern at specific loci or population. After

Bonferroni correction, 6 out 756 pairs of loci were found to deviate significantly from LE

(P<0.05), but again, no specific patterns were detected. As such, all loci were considered to

be reliable, independent estimators of neutral diversity in each of the 36 breeding lines.

2.3.2 Population Genetic Differentiation

Genetic differentiation among stocks (pairwise FST estimates) from lowest to highest,

ranged from 0.00 to 0.29. Mean FST estimates among the 36 stocks were moderate and ranged

from 0.06 (D1 and I1 stocks) to 0.14 (B4 stocks). Overall mean genetic differentiation (FST)

among stocks was 0.09. Genetic differentiation (FST) of stocks from different hatcheries that

had originated from the same geographical region historically was lower compared with the

overall FST, with a mean value of 0.05 within NA1 , 0.01 within NA2, 0.03 within SA1, 0.00

within NA3 and NA4 (Table 2.3). Among 78 pairwise FST comparisons of 13 breeding line

within NA1 group, 53 tests were statistically significant, which indicated most breeding lines

tested within the NA1 group were diverged. While all 28 pairwise FST tests within NA2 group

were not significant, 13 pairwise FST estimates among 15 tests within SA1 group were also

not significant. In addition, no significant differences were observed for pairwise FST

comparisons within NA3, or NA4 respectively. It is interesting to note that 37 of 40 pairwise

FST tests were not significant between breeding lines in China that originated from NA2, NA3

and NA4, suggesting a close genetic relationship among NA2, NA3, and NA4. Pairwise

genetic distance estimates (DA) ranged from 0.03 (I1 vs. J1) to 0.56 (B4 vs. R1). Average

pairwise genetic distance estimates ranged from 0.17 for I1 stocks to 0.41 for R1 stocks

(Table 2.3). Pairwise genetic distance estimates between hatcheries that had originated from


the same geographical region historically also were lower compared with the overall average

DA (0.23), with average DA 0.12 between NA1, 0.11 between NA2, 0.15 between SA1, 0.06

between NA3 and 0.09 between NA4.

61 Chapter 2: Levels of Genetic Diversity and Inferred Origins of Penaeus vannamei Culture Resources in China: Implications for the Production of a Broad Synthetic BasePopulation for Genetic Improvement

Table 2.3 Population genetic differentiation among 36 P. vannamei stocks (ID: name of 36 breeding lines; GP: The 10 places of origin for thesebreeding lines). The pairwise genetic differentiation amongst 36 breeds estimated using FST (Weir and Cockerham, 1984) (below diagonal). Thepairwise genetic distance DA (Nei et al., 1983) between 36 breeds (above diagonal). Values in shaded areas represent comparison amongbreeding lines within same groups of origin. The last two rows on the table represent mean FST and DA values of 36 breeding lines. Values of FSTin bold are statistically significance at α=0.05 after Bonferroni correction.

GP NA1 NA2 SA1 NA3 NA4 LA CN1 CN2 SA2 NA5ID A1 B1 C1 D1 D2 E1 B2 E2 F1 B3 G1 B4 H1 I1 E3 J1 K1 L1 I2 K2 M1 N1 N2 N3 O1 O2 N4 P1 P2 Q1 Q2 R1 S1 T1 U1 V1A1 0.06 0.13 0.12 0.15 0.12 0.13 0.19 0.15 0.12 0.17 0.25 0.24 0.24 0.30 0.25 0.33 0.32 0.30 0.37 0.31 0.35 0.30 0.34 0.30 0.32 0.32 0.26 0.25 0.34 0.34 0.52 0.30 0.35 0.32 0.45B1 0.00 0.08 0.09 0.12 0.08 0.08 0.13 0.11 0.07 0.13 0.20 0.18 0.20 0.24 0.20 0.30 0.25 0.28 0.33 0.26 0.28 0.24 0.29 0.24 0.27 0.28 0.22 0.21 0.28 0.27 0.48 0.27 0.30 0.28 0.42C1 0.07 0.05 0.06 0.12 0.05 0.09 0.11 0.08 0.15 0.13 0.18 0.15 0.17 0.20 0.18 0.24 0.19 0.24 0.26 0.22 0.27 0.23 0.27 0.24 0.25 0.26 0.17 0.18 0.27 0.26 0.50 0.22 0.28 0.24 0.36D1 0.07 0.06 0.03 0.08 0.05 0.09 0.09 0.06 0.15 0.11 0.16 0.13 0.13 0.18 0.15 0.20 0.15 0.19 0.23 0.21 0.30 0.25 0.25 0.22 0.23 0.25 0.14 0.14 0.24 0.24 0.48 0.20 0.27 0.22 0.39D2 0.06 0.06 0.05 0.02 0.09 0.12 0.15 0.10 0.17 0.10 0.20 0.18 0.15 0.16 0.15 0.21 0.15 0.19 0.21 0.20 0.27 0.23 0.22 0.18 0.19 0.19 0.14 0.14 0.20 0.20 0.28 0.18 0.19 0.16 0.31E1 0.08 0.08 0.02 0.05 0.06 0.07 0.10 0.05 0.11 0.09 0.12 0.10 0.13 0.14 0.13 0.18 0.16 0.19 0.22 0.17 0.29 0.23 0.27 0.23 0.25 0.24 0.15 0.14 0.23 0.25 0.50 0.24 0.29 0.24 0.41B2 0.10 0.09 0.04 0.08 0.07 0.05 0.07 0.04 0.11 0.07 0.13 0.10 0.20 0.20 0.20 0.27 0.23 0.27 0.28 0.24 0.24 0.22 0.23 0.21 0.25 0.21 0.21 0.21 0.24 0.27 0.46 0.24 0.29 0.23 0.41E2 0.08 0.07 0.05 0.06 0.06 0.05 0.00 0.06 0.19 0.08 0.17 0.11 0.19 0.19 0.20 0.26 0.21 0.24 0.26 0.23 0.25 0.24 0.22 0.22 0.22 0.22 0.20 0.21 0.24 0.26 0.47 0.22 0.30 0.22 0.37F1 0.08 0.08 0.03 0.03 0.02 0.04 0.02 0.02 0.14 0.06 0.13 0.10 0.15 0.17 0.15 0.20 0.17 0.21 0.23 0.20 0.25 0.21 0.21 0.19 0.22 0.21 0.17 0.18 0.23 0.25 0.47 0.22 0.28 0.22 0.38B3 0.00 0.00 0.00 0.05 0.05 0.03 0.05 0.06 0.05 0.15 0.21 0.21 0.20 0.24 0.19 0.27 0.25 0.24 0.33 0.28 0.28 0.24 0.28 0.21 0.28 0.28 0.22 0.20 0.28 0.26 0.49 0.30 0.32 0.31 0.45G1 0.04 0.04 0.04 0.05 0.02 0.04 0.01 0.00 0.00 0.03 0.18 0.13 0.17 0.18 0.16 0.24 0.18 0.20 0.25 0.23 0.23 0.20 0.19 0.19 0.20 0.20 0.16 0.16 0.20 0.20 0.44 0.23 0.25 0.22 0.35B4 0.11 0.13 0.08 0.12 0.12 0.04 0.08 0.09 0.13 0.10 0.09 0.06 0.27 0.28 0.24 0.31 0.28 0.33 0.36 0.30 0.30 0.26 0.30 0.32 0.34 0.30 0.29 0.29 0.33 0.34 0.56 0.34 0.38 0.33 0.50H1 0.12 0.12 0.04 0.08 0.08 0.00 0.07 0.06 0.07 0.07 0.06 0.02 0.23 0.24 0.23 0.29 0.23 0.30 0.28 0.27 0.29 0.28 0.30 0.28 0.30 0.29 0.23 0.25 0.27 0.28 0.53 0.28 0.33 0.28 0.46I1 0.16 0.15 0.08 0.07 0.05 0.08 0.12 0.09 0.04 0.12 0.07 0.18 0.09 0.05 0.03 0.12 0.10 0.05 0.15 0.10 0.25 0.19 0.27 0.19 0.19 0.24 0.10 0.11 0.17 0.19 0.39 0.19 0.21 0.18 0.33E3 0.16 0.15 0.08 0.08 0.05 0.06 0.09 0.08 0.04 0.11 0.06 0.14 0.07 0.01 0.06 0.12 0.11 0.08 0.17 0.11 0.28 0.22 0.26 0.21 0.22 0.24 0.14 0.12 0.17 0.21 0.37 0.21 0.21 0.17 0.33J1 0.14 0.13 0.07 0.05 0.03 0.07 0.10 0.07 0.03 0.10 0.06 0.15 0.08 0.00 0.01 0.10 0.08 0.07 0.16 0.09 0.25 0.18 0.27 0.21 0.22 0.26 0.11 0.09 0.18 0.19 0.39 0.25 0.23 0.20 0.34K1 0.17 0.17 0.09 0.11 0.06 0.06 0.13 0.13 0.05 0.13 0.08 0.17 0.07 0.03 0.01 0.03 0.10 0.12 0.16 0.11 0.29 0.22 0.30 0.22 0.24 0.28 0.14 0.14 0.25 0.26 0.50 0.31 0.29 0.21 0.38L1 0.16 0.16 0.10 0.06 0.03 0.11 0.13 0.10 0.04 0.15 0.08 0.23 0.13 0.00 0.01 0.00 0.03 0.11 0.15 0.10 0.28 0.21 0.25 0.22 0.22 0.28 0.08 0.07 0.19 0.19 0.38 0.23 0.23 0.18 0.32I2 0.18 0.18 0.11 0.09 0.06 0.11 0.13 0.09 0.07 0.16 0.09 0.21 0.12 0.00 0.02 0.01 0.05 0.00 0.14 0.14 0.29 0.22 0.27 0.25 0.21 0.29 0.11 0.10 0.19 0.17 0.40 0.22 0.24 0.21 0.31K2 0.16 0.19 0.07 0.05 0.01 0.08 0.14 0.08 0.01 0.26 0.08 0.25 0.08 0.00 0.00 0.00 0.00 0.00 0.00 0.11 0.34 0.28 0.30 0.21 0.27 0.28 0.19 0.19 0.23 0.24 0.43 0.26 0.26 0.21 0.31M1 0.18 0.19 0.09 0.07 0.05 0.08 0.11 0.08 0.05 0.17 0.08 0.17 0.07 0.00 0.00 0.00 0.03 0.00 0.00 0.00 0.30 0.22 0.31 0.22 0.26 0.28 0.15 0.11 0.23 0.24 0.42 0.27 0.29 0.22 0.31N1 0.13 0.12 0.11 0.17 0.11 0.15 0.09 0.11 0.09 0.09 0.08 0.17 0.17 0.15 0.13 0.13 0.17 0.17 0.17 0.19 0.18 0.05 0.17 0.15 0.13 0.20 0.28 0.31 0.29 0.27 0.43 0.26 0.30 0.23 0.37N2 0.09 0.10 0.09 0.14 0.08 0.11 0.09 0.10 0.07 0.06 0.06 0.15 0.14 0.11 0.09 0.09 0.11 0.13 0.13 0.19 0.15 0.00 0.16 0.16 0.13 0.20 0.22 0.22 0.25 0.24 0.40 0.26 0.27 0.21 0.33N3 0.11 0.12 0.08 0.12 0.05 0.11 0.09 0.09 0.03 0.09 0.05 0.18 0.13 0.09 0.08 0.08 0.09 0.10 0.10 0.10 0.12 0.03 0.02 0.15 0.18 0.14 0.29 0.25 0.21 0.20 0.38 0.23 0.25 0.20 0.33O1 0.12 0.12 0.08 0.10 0.04 0.11 0.11 0.09 0.03 0.10 0.06 0.19 0.13 0.03 0.04 0.03 0.05 0.04 0.03 0.01 0.06 0.06 0.03 0.01 0.15 0.15 0.25 0.26 0.24 0.24 0.36 0.21 0.28 0.19 0.35O2 0.09 0.09 0.08 0.10 0.04 0.10 0.07 0.07 0.04 0.06 0.04 0.18 0.14 0.07 0.07 0.07 0.09 0.08 0.09 0.08 0.11 0.04 0.02 0.02 0.01 0.18 0.17 0.23 0.18 0.18 0.34 0.15 0.20 0.14 0.30N4 0.17 0.17 0.12 0.15 0.08 0.17 0.10 0.13 0.05 0.15 0.09 0.24 0.19 0.12 0.10 0.11 0.13 0.13 0.14 0.15 0.16 0.05 0.06 0.00 0.04 0.03 0.28 0.28 0.22 0.25 0.34 0.20 0.21 0.16 0.40P1 0.13 0.12 0.07 0.04 0.03 0.07 0.11 0.09 0.04 0.09 0.07 0.18 0.10 0.01 0.02 0.01 0.03 0.00 0.03 0.00 0.02 0.17 0.13 0.11 0.05 0.08 0.14 0.06 0.15 0.14 0.40 0.19 0.19 0.19 0.33P2 0.12 0.11 0.07 0.04 0.03 0.06 0.09 0.06 0.04 0.08 0.04 0.13 0.07 0.01 0.01 0.00 0.03 0.00 0.00 0.00 0.00 0.13 0.08 0.08 0.03 0.06 0.13 0.00 0.17 0.17 0.40 0.22 0.23 0.22 0.34Q1 0.17 0.17 0.10 0.10 0.05 0.11 0.11 0.10 0.04 0.14 0.08 0.23 0.13 0.02 0.01 0.04 0.03 0.02 0.03 0.00 0.04 0.15 0.12 0.08 0.03 0.05 0.08 0.02 0.03 0.09 0.33 0.19 0.11 0.14 0.35Q2 0.14 0.13 0.09 0.08 0.04 0.11 0.11 0.09 0.05 0.08 0.06 0.21 0.13 0.02 0.03 0.03 0.06 0.01 0.01 0.00 0.03 0.13 0.09 0.07 0.02 0.04 0.09 0.01 0.02 0.00 0.34 0.20 0.15 0.19 0.31R1 0.23 0.24 0.22 0.20 0.11 0.24 0.20 0.21 0.16 0.21 0.17 0.29 0.25 0.13 0.12 0.13 0.16 0.11 0.13 0.12 0.15 0.20 0.15 0.16 0.10 0.11 0.16 0.11 0.11 0.10 0.10 0.32 0.26 0.29 0.35S1 0.17 0.16 0.12 0.11 0.07 0.13 0.14 0.12 0.09 0.13 0.10 0.22 0.15 0.03 0.04 0.06 0.09 0.04 0.03 0.00 0.05 0.17 0.13 0.11 0.06 0.08 0.13 0.03 0.05 0.02 0.02 0.09 0.17 0.17 0.34T1 0.16 0.15 0.11 0.10 0.05 0.13 0.13 0.14 0.07 0.11 0.09 0.22 0.16 0.05 0.05 0.07 0.08 0.05 0.07 0.02 0.08 0.16 0.12 0.10 0.06 0.06 0.10 0.03 0.07 0.01 0.02 0.07 0.02 0.11 0.31U1 0.18 0.17 0.12 0.13 0.07 0.13 0.12 0.12 0.05 0.15 0.09 0.24 0.15 0.05 0.05 0.06 0.05 0.04 0.07 0.00 0.07 0.13 0.10 0.05 0.03 0.04 0.04 0.07 0.07 0.01 0.04 0.13 0.07 0.06 0.32V1 0.16 0.16 0.15 0.12 0.07 0.14 0.15 0.12 0.10 0.12 0.09 0.22 0.16 0.07 0.09 0.08 0.11 0.06 0.06 0.00 0.08 0.18 0.13 0.10 0.07 0.09 0.15 0.07 0.06 0.07 0.05 0.10 0.08 0.08 0.11FST 0.12 0.12 0.08 0.08 0.06 0.09 0.10 0.09 0.05 0.09 0.06 0.16 0.11 0.06 0.06 0.06 0.08 0.07 0.08 0.05 0.08 0.13 0.10 0.09 0.06 0.07 0.12 0.06 0.05 0.07 0.06 0.16 0.09 0.09 0.09 0.10DA 0.28 0.23 0.21 0.19 0.18 0.19 0.20 0.21 0.18 0.24 0.19 0.27 0.24 0.17 0.19 0.18 0.23 0.19 0.21 0.24 0.22 0.26 0.23 0.25 0.22 0.22 0.24 0.19 0.19 0.22 0.23 0.41 0.23 0.25 0.21 0.36


A phylogenetic reconstruction of stock (Hatchery) relationships is represented in Figure

2.2 Stocks with a NA1 origin clustered to form a monophyletic clade, as did all stocks in the

SA1 group. NA2 and NA3 stocks together formed a third clade, consistent with pairwise FST

comparisons showing that NA2 and NA3 shared a close genetic relationship. Seven breeding

lines originating from NA4, NA5, LA, SA2, CN1 and CN2 loosely clustered together to form

a fourth, polyphyletic clade. However, due to a lack of multiple lines being available from the

various source populations represented in this clade, we cannot discard the possibility that

each of these lineages is genetically distinct and could potentially be readily identified

through multi-locus analysis.


Figure 2.2: An unrooted neighbour joining tree for 36 P. vannamei breeding lines based on

seven microsatellite loci using Nei’s DA genetic distance method. Each breeding line label

includes the abbreviations of origins and hatchery ID as per Table 2.1.


The most appropriate number of clusters applying the Bayesian method in STRUCTURE

was, k=2 even though k=4 was also strongly supported (see Supplementary Figure S2.1).

With k=2, all stocks that originated from NA1 clustered into a single group with stocks from

LA forming a separate cluster. The other 22 stocks showed an admixture pattern after

Bayesian clustering (Figure 2.3_a). When k=4, all stocks from NA1 and LA clustered into

two discrete genetic populations, (Figure 2.3_b) but stocks from NA2 separated to form a

separate cluster. Despite a pattern of minor admixture for some stocks from SA1, all SA1

stocks formed a single genetic cluster (Figure 2.3_b). Remaining stocks showed an

admixture pattern. Some individuals from D2 stocks showed a different Bayesian cluster

assignment within populations that suggested that they may constitute a mix of different

genetic resources, historically. Overall, the Bayesian cluster results were consistent with the

population structure analysis based on FST and the reconstructed phylogenetic tree.

Figure 2.3: Individual assignment based on Bayesian analysis of 36 breeding lines at: a)

Structure plot for K=2; b) Structure plot for K=4.

a

b


2.3.3 Relatedness Estimates

There was a high correlation between results from different relatedness estimate methods

(r=0.85). Average relatedness estimates using RQGwithin stocks ranged from 0.02 (R1 and T1)

to 0.32 (B4), with an average value of 0.16. In general, average relatedness values using RDML

were larger than for RQG, and ranged from 0.12 (T1) to 0.4 (B4) with an average estimate of

0.26. Significantly higher values were found for relatedness comparisons within groups than

between different groups (Table 2.4).

66 Chapter 2: Levels of Genetic Diversity and Inferred Origins of Penaeus vannamei Culture Resources in

China: Implications for the Production of a Broad Synthetic Base Population for Genetic Improvement

Table 2.4 Average relatedness estimates amongst 36 P. vannamei stocks. The pairwise relatedness estimates amongst 36 breeds calculated using

RQG (Queller and Goodnight, 1989) (above diagonal). The pairwise relatedness estimates of RDML (Milligan, 2003) between 36 breeds (below

diagonal). Tables in shadow areas were the comparison between same groups of origin. The last two rows on the table represent average RQG and

RDML of 36 batches breeds.

ID A1 B1 C1 D1 D2 E1 B2 E2 F1 B3 G1 B4 H1 I1 E3 J1 K1 L1 I2 K2 M1 N1 N2 N3 O1 O2 N4 P1 P2 Q1 Q2 R1 S1 T1 U1 V1A1 0.20 0.09 0.07 0.04 0.10 0.06 0.05 0.03 0.19 0.05 0.05 0.00 -0.03 -0.09 -0.03 -0.08 -0.10 -0.04 -0.10 -0.08 -0.01 -0.01 -0.04 -0.04 -0.05 -0.02 -0.05 -0.03 -0.13 -0.09 -0.15 -0.08 -0.11 -0.12 -0.10

B1 0.31 0.11 0.08 0.04 0.12 0.10 0.08 0.06 0.24 0.05 0.08 0.04 -0.02 -0.04 0.00 -0.10 -0.08 -0.05 -0.08 -0.02 0.00 0.01 -0.03 -0.01 -0.04 -0.02 -0.06 -0.02 -0.09 -0.07 -0.13 -0.09 -0.11 -0.09 -0.09

C1 0.22 0.24 0.12 0.05 0.14 0.11 0.11 0.10 0.09 0.04 0.11 0.11 0.01 0.01 0.02 0.00 0.00 -0.03 0.03 0.05 0.02 0.00 0.01 0.03 -0.03 0.02 0.00 -0.01 -0.06 -0.06 -0.13 -0.03 -0.07 -0.02 -0.03

D1 0.22 0.22 0.24 0.07 0.13 0.08 0.12 0.10 0.06 0.04 0.09 0.09 0.03 0.02 0.03 0.00 0.04 0.01 0.04 0.03 -0.07 -0.06 -0.02 0.01 -0.06 -0.01 0.03 0.04 -0.05 -0.05 -0.14 -0.02 -0.09 -0.04 -0.07

D2 0.19 0.19 0.18 0.20 0.06 0.02 0.02 0.04 0.01 0.01 0.00 0.00 0.00 -0.01 0.00 -0.03 0.00 -0.02 0.01 0.01 -0.06 -0.06 -0.02 0.01 -0.04 0.01 -0.01 0.00 -0.04 -0.05 -0.03 -0.02 -0.06 -0.03 -0.03

E1 0.23 0.25 0.25 0.26 0.19 0.12 0.10 0.10 0.14 0.07 0.16 0.15 0.04 0.05 0.04 0.05 0.00 0.02 0.06 0.07 -0.06 -0.03 -0.04 0.00 -0.06 -0.01 0.00 0.04 -0.04 -0.07 -0.15 -0.04 -0.10 -0.04 -0.09

B2 0.20 0.24 0.22 0.19 0.15 0.23 0.18 0.14 0.12 0.09 0.15 0.15 -0.01 0.02 0.00 -0.05 -0.04 -0.05 -0.03 0.02 0.03 0.01 0.01 0.04 -0.03 0.03 -0.05 -0.02 -0.04 -0.07 -0.10 -0.04 -0.09 -0.02 -0.09

E2 0.19 0.21 0.22 0.23 0.16 0.22 0.29 0.14 0.07 0.11 0.13 0.14 0.00 0.03 0.00 -0.04 -0.01 -0.04 0.03 0.03 0.03 0.01 0.05 0.08 0.00 0.04 -0.03 -0.02 -0.05 -0.05 -0.12 -0.02 -0.09 0.00 -0.02

F1 0.18 0.20 0.21 0.21 0.16 0.22 0.26 0.26 0.06 0.07 0.10 0.12 0.02 0.03 0.02 0.01 0.01 -0.02 0.05 0.05 0.00 -0.02 0.02 0.07 -0.03 0.05 -0.02 -0.02 -0.03 -0.05 -0.11 -0.03 -0.08 0.01 -0.08

B3 0.30 0.36 0.21 0.20 0.16 0.25 0.25 0.21 0.19 0.07 0.11 0.06 0.00 -0.02 0.03 -0.05 -0.08 0.00 -0.07 -0.04 0.00 0.02 -0.03 -0.01 -0.06 -0.03 -0.07 -0.01 -0.08 -0.06 -0.14 -0.10 -0.11 -0.10 -0.12

G1 0.19 0.20 0.17 0.17 0.15 0.19 0.22 0.22 0.20 0.21 0.04 0.06 -0.01 -0.01 -0.01 -0.03 -0.03 -0.02 0.00 -0.02 -0.01 -0.02 0.01 0.03 -0.03 0.02 -0.03 -0.02 -0.05 -0.05 -0.12 -0.05 -0.09 -0.05 -0.07

B4 0.18 0.21 0.22 0.22 0.16 0.27 0.25 0.24 0.22 0.22 0.17 0.28 -0.03 0.00 0.01 0.01 -0.04 -0.04 -0.04 0.00 0.03 0.05 -0.02 -0.05 -0.10 -0.05 -0.06 -0.02 -0.09 -0.09 -0.17 -0.08 -0.13 -0.08 -0.15

H1 0.15 0.17 0.20 0.20 0.15 0.25 0.24 0.24 0.22 0.17 0.19 0.35 -0.01 0.03 0.00 0.02 0.00 -0.05 0.01 0.03 -0.02 -0.01 -0.03 -0.03 -0.10 -0.06 -0.03 -0.02 -0.06 -0.07 -0.17 -0.03 -0.11 -0.03 -0.13

I1 0.13 0.14 0.13 0.15 0.12 0.17 0.12 0.12 0.14 0.15 0.12 0.12 0.11 0.08 0.10 0.08 0.05 0.12 0.14 0.09 -0.03 -0.03 -0.03 0.05 -0.03 0.00 0.04 0.04 0.02 0.01 -0.05 0.01 -0.03 0.01 -0.02

E3 0.08 0.12 0.12 0.13 0.11 0.17 0.14 0.14 0.13 0.13 0.12 0.13 0.13 0.18 0.08 0.09 0.06 0.08 0.11 0.10 -0.05 -0.03 -0.03 0.03 -0.03 0.00 0.02 0.04 0.03 -0.01 -0.03 0.01 -0.02 0.02 -0.02

J1 0.13 0.16 0.15 0.16 0.13 0.18 0.13 0.13 0.14 0.18 0.14 0.15 0.13 0.20 0.19 0.08 0.08 0.10 0.11 0.10 -0.03 -0.02 -0.03 0.01 -0.05 -0.02 0.03 0.07 0.01 0.00 -0.04 -0.03 -0.04 -0.01 -0.03

K1 0.09 0.09 0.13 0.14 0.11 0.18 0.10 0.11 0.14 0.12 0.11 0.14 0.13 0.17 0.20 0.20 0.09 0.11 0.18 0.10 -0.02 -0.02 -0.01 0.06 -0.04 -0.01 0.05 0.04 0.00 -0.02 -0.10 -0.04 -0.04 -0.04 -0.05

L1 0.09 0.11 0.13 0.16 0.13 0.15 0.11 0.13 0.13 0.11 0.12 0.11 0.12 0.16 0.17 0.19 0.20 0.05 0.13 0.09 -0.05 -0.04 -0.01 0.03 -0.04 -0.03 0.06 0.08 0.01 0.01 -0.04 0.00 -0.03 0.02 -0.02

I2 0.12 0.11 0.11 0.14 0.12 0.15 0.11 0.11 0.12 0.16 0.12 0.11 0.09 0.22 0.19 0.21 0.22 0.18 0.15 0.08 -0.06 -0.04 -0.04 0.00 -0.03 -0.02 0.04 0.07 0.02 0.04 -0.04 0.01 -0.04 -0.01 0.00K2 0.10 0.10 0.15 0.16 0.14 0.18 0.11 0.14 0.17 0.10 0.13 0.12 0.12 0.22 0.19 0.21 0.26 0.22 0.24 0.21 -0.04 -0.04 0.04 0.16 -0.01 0.02 0.07 0.05 0.05 0.04 -0.07 0.04 -0.02 0.07 0.04M1 0.09 0.13 0.16 0.14 0.13 0.18 0.14 0.14 0.16 0.12 0.12 0.15 0.14 0.19 0.19 0.20 0.21 0.20 0.18 0.29 -0.05 -0.03 -0.02 0.07 -0.03 -0.01 0.05 0.07 0.02 0.00 -0.04 0.02 -0.04 0.02 0.02N1 0.13 0.14 0.14 0.09 0.09 0.10 0.16 0.15 0.13 0.14 0.12 0.16 0.14 0.11 0.10 0.12 0.12 0.11 0.12 0.11 0.10 0.22 0.14 0.14 0.11 0.09 -0.07 -0.08 -0.06 -0.03 -0.07 -0.04 -0.06 0.02 -0.04

N2 0.14 0.15 0.14 0.10 0.10 0.13 0.14 0.14 0.12 0.15 0.12 0.17 0.14 0.12 0.12 0.14 0.14 0.13 0.13 0.12 0.12 0.32 0.09 0.08 0.06 0.04 -0.05 -0.04 -0.06 -0.04 -0.07 -0.05 -0.08 -0.01 -0.05

N3 0.11 0.13 0.12 0.12 0.12 0.12 0.15 0.17 0.15 0.12 0.14 0.14 0.12 0.10 0.10 0.11 0.11 0.11 0.10 0.14 0.11 0.22 0.20 0.18 0.06 0.13 -0.06 -0.06 -0.02 0.00 -0.06 -0.01 -0.03 0.05 0.02O1 0.11 0.14 0.14 0.13 0.13 0.14 0.16 0.17 0.18 0.15 0.15 0.12 0.12 0.15 0.14 0.13 0.17 0.14 0.15 0.23 0.17 0.22 0.20 0.24 0.08 0.13 -0.02 -0.05 0.00 0.01 -0.03 0.03 -0.02 0.06 0.04O2 0.11 0.12 0.12 0.10 0.11 0.11 0.11 0.12 0.11 0.11 0.12 0.09 0.09 0.12 0.11 0.11 0.11 0.11 0.12 0.14 0.12 0.22 0.19 0.17 0.19 0.06 -0.05 -0.06 -0.03 -0.02 -0.03 0.00 -0.05 0.01 0.01N4 0.12 0.12 0.12 0.11 0.13 0.12 0.15 0.15 0.15 0.11 0.14 0.12 0.09 0.11 0.11 0.10 0.11 0.09 0.10 0.12 0.11 0.19 0.17 0.22 0.21 0.16 -0.05 -0.06 0.00 -0.01 -0.02 -0.01 -0.02 0.06 -0.03

P1 0.12 0.12 0.14 0.17 0.13 0.15 0.09 0.12 0.12 0.11 0.11 0.11 0.11 0.15 0.13 0.16 0.17 0.17 0.16 0.17 0.17 0.09 0.11 0.08 0.10 0.11 0.08 0.07 0.01 0.01 -0.05 0.01 -0.02 -0.02 -0.02

P2 0.12 0.14 0.13 0.16 0.13 0.17 0.13 0.12 0.12 0.14 0.12 0.12 0.11 0.16 0.15 0.19 0.18 0.20 0.19 0.17 0.18 0.09 0.13 0.10 0.11 0.09 0.09 0.19 0.00 -0.01 -0.05 -0.02 -0.05 -0.04 -0.06

Q1 0.07 0.09 0.08 0.09 0.09 0.09 0.10 0.10 0.10 0.09 0.10 0.08 0.09 0.11 0.12 0.12 0.11 0.11 0.13 0.14 0.12 0.09 0.09 0.11 0.11 0.10 0.10 0.12 0.12 0.03 -0.01 0.01 0.01 0.02 -0.03

Q2 0.09 0.11 0.10 0.11 0.10 0.10 0.10 0.11 0.10 0.12 0.11 0.10 0.10 0.12 0.11 0.13 0.10 0.13 0.15 0.14 0.12 0.12 0.12 0.14 0.13 0.12 0.11 0.14 0.12 0.14 -0.02 0.00 -0.01 -0.01 0.01R1 0.04 0.06 0.04 0.04 0.11 0.04 0.06 0.05 0.05 0.05 0.05 0.04 0.03 0.07 0.08 0.08 0.04 0.08 0.08 0.07 0.08 0.08 0.09 0.09 0.09 0.10 0.10 0.07 0.08 0.09 0.10 -0.01 0.02 -0.02 0.03S1 0.10 0.09 0.11 0.12 0.11 0.11 0.10 0.11 0.10 0.09 0.09 0.09 0.10 0.13 0.12 0.10 0.09 0.12 0.13 0.14 0.14 0.11 0.11 0.12 0.15 0.14 0.12 0.13 0.12 0.12 0.13 0.11 0.00 0.00 -0.02

T1 0.07 0.07 0.07 0.06 0.08 0.06 0.06 0.06 0.06 0.07 0.07 0.06 0.05 0.09 0.09 0.09 0.08 0.08 0.09 0.08 0.08 0.07 0.08 0.09 0.08 0.08 0.09 0.09 0.08 0.10 0.10 0.11 0.12 -0.01 -0.02

U1 0.07 0.09 0.09 0.09 0.10 0.09 0.11 0.12 0.12 0.07 0.09 0.08 0.09 0.11 0.11 0.10 0.13 0.11 0.11 0.15 0.12 0.13 0.12 0.14 0.15 0.13 0.14 0.09 0.09 0.11 0.11 0.10 0.11 0.10 -0.02

V1 0.06 0.06 0.09 0.06 0.09 0.06 0.07 0.08 0.07 0.05 0.07 0.04 0.04 0.09 0.08 0.09 0.07 0.10 0.12 0.12 0.11 0.07 0.08 0.10 0.09 0.10 0.07 0.10 0.09 0.07 0.10 0.12 0.09 0.07 0.08DadML 0.35 0.36 0.29 0.30 0.19 0.29 0.28 0.26 0.22 0.36 0.19 0.40 0.35 0.20 0.20 0.24 0.31 0.19 0.23 0.35 0.27 0.37 0.25 0.28 0.33 0.22 0.23 0.19 0.21 0.13 0.16 0.29 0.23 0.12 0.17 0.26QuellerGt 0.26 0.24 0.17 0.18 0.04 0.19 0.18 0.22 0.11 0.25 0.06 0.32 0.28 0.10 0.10 0.12 0.21 0.08 0.13 0.30 0.18 0.30 0.13 0.21 0.28 0.11 0.16 0.07 0.09 0.04 0.02 0.21 0.11 0.02 0.10 0.22


2.3.4 Effective Population Size (Ne)

Based on the linkage model, effective population size (Ne1) estimates ranged from

19.9 to 749.6, with four populations recorded as infinite. Applying the molecular co-

ancestry method (Ne2), most stocks showed estimates (Ne2) that were much lower

than for Ne1 and ranged from 5.0 to 68.6. Nine populations for Ne2 were infinite,

indicating that their Ne size was very large (Table 2.2). Infinite Ne estimates

generated with both Ne1 or Ne2 estimators may result from either mixing of stocks

from different genetic stocks in history (Hartl and Clark, 1997) or analysis of small

sample sizes per line. A parallel result has been reported for Ne estimates in captive

barramundi stocks where lines with small sample sizes were estimated to be infinite

(Loughnan et al., 2016).

2.4 DISCUSSION

2.4.1 Genetic Variation Levels Within and Among Stocks

The current project provides the most comprehensive analysis available for the

status of genetic diversity levels and inferred genetic origins of farmed P. vannamei

broodstocks in China. The results presented here can also be used as a reference and

foundation for assessing genetic resources of P. vannamei stocks in other countries in

Asia where this species is becoming the major farmed penaeid.

Overall, levels of genetic diversity in 36 farm stocks assessed here were similar

compared with another earlier, but less comprehensive study on genetic diversity in

P. vannamei stocks in China (Zhang et al., 2014). The small differences observed



between results obtained in the two studies most likely reflect the different panel of

microsatellite loci used to assess diversity in the stocks examined. In general, genetic

diversity levels in farm stocks of P. vannamei in China were slightly higher when

compared with stocks farmed in Mexico (Perez-Enriquez et al., 2009; Vela Avitúa et

al., 2013), consistent with the fact that genetic resources in Mexican hatcheries are

believed to have come from a single origin (i.e. the ‘Los Malago’ breeding line that

forms the basis of the industry there (Perez-Enriquez et al., 2009)). In contrast in

China, farm stocks have been developed from a number of different sources that

originated from different geographical locations (both from the native and introduced

ranges) before they were introduced to China.

Among the breeding lines examined here, estimates of genetic diversity in stocks

that originated from NA1, NA2 and SA1 sources in general, were lower compared

with others (Table 2.2). Potentially, this difference may reflect the longer time span

they have spent managed as closed breeding lines (Benzie, 2009) or differences in

relative levels of genetic variation present in the initial founders in each breeding

program. It is interesting to note that genetic diversity levels in the CN2 stocks were

much higher than in other Chinese stocks tested here (Table 2.2). Broodstock

management practices and strategies employed in the CN2 hatchery are different

from many others in China. First, the number of broodstock maintained in the CN2

hatchery is significantly higher than in other hatcheries, with approximately 15,000

to 25,000 pairs maintained each year for PL production. Assuming that mating

success approaches 10% per night during the nauplii supply season, this implies that

1,500 to 2,500 pairs likely contribute to each larval batch. Assuming that breeders

are unrelated and contributions from pairs are approximately equal, this number of

breeders can maintain a high effective population size and avoid genetic erosion of


the population due to genetic drift effects. Each year the CN2 hatchery also obtains a

small proportion of new stock from other hatcheries and thoroughly mixes their

diversity into their own stocks, introducing new alleles to their breeding program.

Inbreeding levels (Fis) were similar to those reported in an earlier study of P.

vannamei in China (Zhang et al., 2014). These estimates suggest that most

broodstocks currently used at hatcheries in China likely apply effective inbreeding

management and follow the recommendations proposed by (Moss et al., 2007) who

stated that inbreeding levels should not exceed 10%. Chinese commercial

broodstocks show much lower inbreeding levels compared with those reported in

farmed P. vannamei stocks in Mexico (Fis 0.25) (Perez-Enriquez et al., 2009) and

Brazil (Fis 0.30) (Maggioni et al., 2013). It should be noted however, that while high

homozygosity in two studies mentioned above may reflect significant inbreeding,

deviations from HWE more likely suggests that homozygote excess was a function

of null alleles (a common problem with microsatellite studies) and that inbreeding in

these studies may have been overestimated. Inbreeding coefficient Fis estimates in a

number of the Chinese stocks tested here however, were much higher than that

reported in the stocks imported originally. A possible explanation for this

observation is that while they probably received a representative gene pool initially,

poor management of broodstock genetic resources subsequently over time may

account for observed high Fis estimates in these stocks. Notably, Ft (mean Ft = 0.21)

and Fd (mean Fd = 0.22) estimates for inbreeding level were three times higher than

the Fis estimates (mean Fis= 0.07). Doyle (2016) argued that Fis can be a poor

indicator of inbreeding level in stocks. In our study, Fd and Ft estimates were much

higher than for the more widely used Fis estimates, a difference that could suggest a



more serious inbreeding issue in Chinese stocks. We must be cautious however, in

concluding that stocks are inbred based solely on Fd or Ft estimates, as maximum

likelihood methods can overestimate the real value, particularly for stocks with low

relationship estimates (Milligan, 2003). The essential point about estimating

inbreeding level is that it will always be a relative estimate and not an absolute value,

so there needs to be some comparison point rather than a discussion based solely on

the inbreeding estimation method that is employed. For example, in order to

acknowledge any real decline in genetic variation in captive populations of P.

vannamei, ideally we would need a comparison point with a reference wild

population (Vela Avitúa et al., 2013). While this was not possible in the current study,

for the purpose of genetic monitoring of broodstocks of P. vannamei in closed

rearing systems in China, we can obtain a relative assessment of the success or

otherwise of management practices employed to manage genetic diversity over time

by sampling successive broodstock cohorts over years in the same hatchery using the

same marker loci (De Lima et al., 2008). Based on Fis comparisons with that in

Mexico (Perez-Enriquez et al., 2009) and Brazil (De Lima et al., 2008), there is an

indication that management of genetic diversity levels in China may have been better

than in hatcheries elsewhere.

2.4.2 Population Differentiation and Origins of Genetic Resources

The estimate of mean level of genetic differentiation (FST) among Chinese P.

vannamei culture stocks was 0.09 (Table 2.3) indicating a moderate degree of allelic

heterogeneity among stocks. This result is consistent with those reported in previous

studies in China (FST, 0.08) (Zhang et al., 2014) and Brazil (FST, 0.06) (Maggioni et

al., 2013), but were higher than estimates reported for stocks in Mexico (FST, 0.02)

(Perez-Enriquez et al., 2009). Differences reflect that Mexican P. vannamei culture


stocks have had a common ancestry, while stocks in Brazil and China most likely

have resulted from mixing of multiple lineages following the importation of exotic

germplasm because no native wild stocks were available in China.

Genetic differentiation estimates among the 36 Chinese stocks showed pairwise

FST estimates ranging from 0.00 to 0.29 (Table 2.3). Stocks with the same inferred

wild origin as expected showed the lowest pairwise FST estimates, (average of 0.05

among NA1, 0.01 among NA2, 0.03 among SA1, 0.00 between NA3 and 0.00

between NA4). Most pairwise FST tests within NA2 or SA1 groups (Table 2.3,

shadowed areas) were low and not significant, which indicates that breeding lines

from different hatcheries in China with the same origins (either from NA2 or SA1)

show limited genetic differentiation. In contrast, breeding lines in China with NA1

origins show moderate levels of genetic heterogeneity. This potentially results from

several independent nucleus breeding centres utilising NA1 stocks. Of interest,

pairwise FST estimates between NA2, NA3 and NA4 were low, indicating a common

ancestry for these groups. When the founding information for NA2 and NA3 was

traced back, we established that both had been developed independently from the

Kona line in Hawaii. Population NA4 was sourced from a Hawaian population but

information was not available as to whether this was from the Kona line or other

lines maintained in Hawaii. While results of the STRUCTURE analysis here provide

little confidence for a significant contribution from the Kona line in NA4, FST

analysis shows relatively low levels of differentiation among NA2, 3 and 4. This

suggests that while NA2 and NA3 had a common Kona line ancestry, NA4 resulted

from mixing of Kona line genes with those from other lines.



In the present study, the unrooted neighbour-joining dendrogram is consistent

with the historical records of P. vannamei culture stocks origins in China (Figure

2.2). Four main clades were distinguished: i) NA1; ii) NA2 and NA3; iii) SA1; and

iv) a clade representing all other stocks. It is reasonable to suggest that NA2 and

NA3 grouped together in a single clade because they were both derived from a

common founding population (the Kona line developed in Hawaii). China has been

the major P. vannamei producer for decades, and there have been many records of

importations since 1987 (Li et al., 2006a). When we combine our results from the

Bayesian cluster analysis with those from a review paper of global exchange of

penaeid shrimps by Benzie (2009) and information from shrimp companies and

research institutes in China regarding the origins of their founding populations, we

can synthesize a more detailed account of founding genetic resources for each group.

Founding populations of NA1 came from Mexico (north Sinaloa) and from

Ecuador, while the NA2 and NA3 lines originated from genetic resources sourced

from the Kona line in Hawaii. A recent study on seven culture populations of P.

vannamei in China (Zhang et al., 2014) indicated the two clusters in their

phylogenetic reconstruction of genetic relationships align with our clades i and ii.

Origins of SA1 are more obscure, but potentially may have had contributions from

stocks in the USA and South America. According to the records, the LA stock was

developed from multiple founding populations that were sourced from wild

populations in South America. The official records for the founding population for

CN1 suggest that they are the result of the mixing of several cultured genetic lines

from the USA and a wild population from Ecuador. SA2 was developed from

germplasm sourced from Hawaii and a Kentucky culture population, confirmed by

its mixed ancestry in the STRUCTURE analysis. The founding population for NA5


came from Texas but originated from South America. CN2, the stock with the

highest diversity, was developed from a mixture of Chinese culture populations that

includes genetic resources possibly from many stocks introduced to China, but

predominantly displaying a Latin American ancestry in the STRUCTURE analysis.

While no official information was available about the origins of the NA4 population,

the STRUCTURE analysis suggests that it likely had a base population developed

from several lines from the USA and South America. The only available information

about the origin of the NA4 line was that it was constructed from a combination of

Hawaiian resources and a special specific pathogen resistant (SPR) line. According

to a history of genetic improvement of P. vannamei culture stocks (Cock et al., 2009),

SPR resources originated from survivors of stocks in commercial ponds in South

America sourced from Ecuador and Colombia in the mid-1990s when Taura

syndrome virus (TSV) was prevalent in the shrimp industry. The broodstock

generated from the survivors apparently possessed useful genetic variation for TSV

resistance and this was applied in a family selection program in Hawaii.

In our study, all of the stocks that have South American origins (i.e. NA4, LA,

CN2, SA2, & NA5), either as a uniform or admixed population following Bayesian

clustering (k=4), showed significantly higher numbers of private alleles (Table 2.2).

Given the very limited information available on the genetic background of virtually

all stocks sampled here and the broad geographical distribution of natural

populations of the target species, the markers appear to have traced the ancestry of

the lines quite successfully. Of interest, assignment of individuals from the D2 stock

(for either k = 2 or 4), suggested that it consisted of two distinct breeding lines that

have been kept isolated from each other (i.e. there is no pattern of admixture between



groups). While all individuals sampled for this study were collected at the same time

from breeding tanks from this hatchery, broodstock used for nauplii production can

however, be between 12 to 16 weeks old after eyestalk ablation and sometimes

hatchery managers keep healthy females from previous hatchery runs and mix them

with broodstock that are younger. Theoretically, a pattern of two different lines

without admixture could occur if the sets of genotypes in the two age groups differ

significantly by chance. In the current situation however, there are records of

importation of two different lines that clearly assign to the NA1 and LA groups. It is

obvious that broodstock from these two lines have been kept separate within the

hatchery.

2.4.3 Implications for Forming a Genetic Foundation (Base) Population for

Genetic Improvement

Our results provide a strong foundation for making decisions about how to form a

synthetic base population of an exotic aquaculture species, P. vannamei with high

levels of genetic variation for a stock improvement program in China. Assuming that

a classical complete diallel cross (Gjedrem and Robinson, 2014; Hung et al., 2014;

Nguyen, 2016) will be conducted to establish the base population, the first question

will be how to identify discrete subpopulations of the available stocks. Results from

the genetic differentiation analyses (pairwise FST, Da, phylogenetic tree and Bayesian

clustering) of the available domesticated stocks of P. vannamei indicate four discrete

subpopulations, NA1, Kona line, SA1 and Latin America (LA) closed group. While

at present there is no available information on additive genetic variance of

commercially important traits for these groups in China, such an experiment tested

that the Kona line is known to have been selected to improve growth performance

but performed poorly for resistance against WSSV (Cuéllar-Anjel et al., 2012). All of


the four subpopulations described here however, have been used for shrimp farming

in China, widely. We suggest that the best approach will be to undertake a complete

4×4 diallel cross with equal offspring contributed from each of the four

subpopulations. This approach will capture not only high levels of genetic variation,

as the stocks are successful domesticated lines they will also carry copies of different

QTL alleles that have been selected indirectly, potentially also including alleles

influencing growth and survival traits (Sae‐Lim et al., 2016) as an outcome of past

domestication and farming processes. Stochastic simulation has suggested that using

four subpopulations to form the base population will maximise genetic gains in

future generations of selection while saving sampling costs associated with using a

greater number of lines in the base population (Holtsmark et al., 2006).

Relatedness estimates generated in the current study can provide a guideline for

avoiding inbreeding risk across the proposed generations of selection. Based on RQG

relatedness index results (Table 2.4), the proposed diallel cross mating design among

stocks should theoretically maintain RQG near zero because none of the four

subpopulations identified for inclusion in the base population have shared a recent

common ancestry as a result of many generations of isolation during their

domestication process. While initially there had been potential for higher rates of

inbreeding for mating pairs within subpopulations, this can be addressed by sourcing

stocks of the same groups from different hatcheries with high genetic diversity levels

for mating. By incorporating relatedness information, and clear pedigree records, any

risk of significant inbreeding potentially leading to inbreeding depression and any

negative impacts of genetic drift can be minimised.



2.5 CONCLUSIONS

Here we have documented genetic diversity levels, genetic differentiation among,

and relatedness patterns of, P. vannamei culture lines currently available in China.

Our results confirm that modern P. vannamei hatchery lines have come from

multiple genetic sources and the genetic differentiation patterns described in hatchery

lines in China are consistent with the historical records of introductions. The data

described here provide foundation information for both hatchery stock management

and for developing a base population with high levels of genetic diversity for a future

breed improvement program.

77 Chapter 3: Genetic parameters for growth and survival traits in a base population of Pacific white shrimp(Penaeus vannamei) developed from domesticated strains in China

s

Chapter 3: Genetic parameters for growth and survival traits in

a base population of Pacific white shrimp (Penaeus vannamei)

developed from domesticated strains in China

ABSTRACT

Historically, breeding programs directed at genetic improvement of penaeid

shrimp farm lines have differed remarkably in the eastern and western hemispheres

with respect to the source of their base stock. In the East, broodstocks were

commonly sourced from wild populations, while the majority of culture industries in

the West are based on domesticated strains with genotype/pedigree information

applied where available. While the majority of genetically improved broodstock used

in shrimp farming around the world have been supplied from these programs in the

West, it is essential to consider how much genetic variation correlated with important

commercial phenotypic traits was captured when the breeding lines were developed

from the available domesticated strains because this provides the fundamental

resource on which the program will depend long-term. In an earlier study, we applied

a complete diallel cross approach to produce a base population for genetic selection

from a number of domesticated P. vannamei strains in China based on their relative

levels of microsatellite variation. Here, we assess quantitative data on growth traits in

families generated in the full 4 x 4 diallel cross. In total, body weight was measured

at two ages (BW1 and BW2) from 2,752 and 2,452 individuals respectively from 89

full-sib families and analysed using a univariate animal model following REML

methodology. Estimated heritabilities (h2 ± SE) for BW1, BW2 and survival (S) were

0.52 ± 0.09, 0.44 ± 0.07, and 0.01 ± 0.02, respectively. The genetic correlation


between growth traits (BW1 and BW2) was 0.95 ± 0.03, a result significantly

different from zero. Genetic correlations between survival and body weight were low

however, 0.26 (S vs BW1) and 0.18 (S vs BW2) respectively, and not significantly

different from zero. High heritability estimates for growth traits confirm that a

substantial component of additive genetic variance is available for growth in our P.

vannamei culture line families in China prior to a family selection breeding program

to improve relative productivity.

Keywords: Penaeus vannamei, Strain, Heritability, Genetic parameters, Body weight


3.1 INTRODUCTION

Opinions on how to source foundation stocks for genetic improvement of penaeid

shrimp culture lines differ remarkably in the ‘West’ and the ‘East’ (Boyd et al.,

2006). The majority of penaeid genetic improvement programs in the West have

developed breeding lines that have been based on available farm strains of

domesticated shrimp often guided by genotypic information where it was available.

In contrast, in the East, animal breeders have preferred to source foundation stocks

directly from wild populations (Boyd et al., 2006), often where there was only

limited or even no knowledge of the genetic attributes of the source populations.

While 87% of annual global farmed shrimp production (estimated at > 3.0 million

tonnes) is produced in the East (FAO, 2016c), specifically in the Asia-Pacific region,

there continues to be a significant shortage of available genetically improved farm

broodstock for this important industry with current annual demand for P. vannamei

broodstock estimated at approximately 1 million pairs (Giltterle and Diener, 2014).

Most broodstock in Asia are currently imported from commercial companies in the

West.

Despite there having been considerable progress made in the Asia-Pacific region

with selective breeding of native penaeid species e.g. P. chinensis (Sui et al., 2016a),

P. indicus (Benzie, 2009) and P. monodon (Krishna et al., 2011)), it will take some

time for current breeding programs to be able to provide sufficient numbers of

genetically improved stock in terms of both quality and quantity to meet demand

from the shrimp farming industry across this region. For ongoing sustainable

development of shrimp farming in the Asia-Pacific region, it is important to

acknowledge and apply lessons learned in earlier genetic improvement programs


conducted on penaeid farm stocks in the West. Essentially, genetic selection is an

artificial breeding strategy that exploits the additive genetic variance that is present

in a breeding line. Therefore, to a significant extent the total amount of genetic

variation that is captured in a founding population will largely determine the relative

success or failure of a breeding program over extended time frames (Falconer and

Mackay, 1996; Gjedrem and Robinson, 2014; Nguyen, 2016). As a consequence,

when existing unimproved domesticated strains are used as founding stocks for a

breed improvement program, an important first step can be to apply a “genotypic

approach” to determine the relationship between levels of genetic diversity in neutral

markers (SNPs/SSR) and genetic variance of commercially-important phenotypic

traits, in particular how much genetic variance that influences growth and survival

traits was captured in the base population.

Applying genetic theory and protocols to capture broad genetic variation in

founding stocks of cultured aquatic species is a relatively recent development (Hayes

et al., 2006). In addition, much of the available literature on this subject has been

based primarily on simulation studies (Fernández et al., 2014; Gianola and Rosa,

2015; Holtsmark et al., 2006; Holtsmark et al., 2008a; b), with very few studies

completed on actual aquaculture breeding programs that apply or test the above

approaches. While several well planned breeding programs undertaken on aquatic

species have been completed that have involved sophisticated strain comparison

experiments prior to starting a selection program (e.g. GIFT tilapia - (Eknath et al.,

1998); Atlantic salmon - (Gjedrem et al., 1991); European sea bass - (Vandeputte et

al., 2014)), significant financial outlays and extended timeframes were required in

each case, illustrating that there are still major issues when pursuing genetic

improvement of aquatic animals more widely. Neutral molecular markers (e.g.


microsatellites) can provide information about relative levels of genetic diversity,

population structure, stock relatedness and kinship in both cost and time effective

ways. This technology has been applied successfully in stock conservation and

management of breeding programs in a number of terrestrial domesticated farm

animals (Carvalho et al., 2015; Revidatti et al., 2014; Wilkinson et al., 2011;

Wilkinson et al., 2012). For aquatic farm species however, to date there are only

relatively few examples where genotype information on domesticated strains has

been included in genetic improvement programs (FAO, 2011).

Different opinions have been offered about the relative merits of using

domesticated strains vs wild stocks as the starting point for producing a base

population for genetic improvement programs. The main advantage of choosing

domesticated strains over wild populations is that locally-sourced farm strains in

general are often much easier to source and much less expensive to collect compared

with wild populations particularly if the target species is an exotic species that can

involve issues with quarantine requirements and large costs associated with sampling

and transportation. Another advantage of domesticated strains over wild strains is

that in most cases they have already accumulated some favourable traits suitable for

culture as a result of the effects of indirect selection in artificial culture environments

over many generations that often make them easier to handle and breed compared

with wild stocks (Olesen et al., 2015). Thirdly, using domesticated strains as the

starting resource can provide a new breeding line with a competitive start, in

particular for commercially important traits including growth performance

(Fernández et al., 2014).


Genetic selection is widely recognised as an efficient tool for improving the

sustainability of fish farming because it can be used to enhance biological production,

feed conversion, and increase survival of aquatic animals (De Verdal et al., 2018;

Gjedrem, 2012; Gjedrem and Rye, 2018; Gjedrem et al., 2012; Nguyen, 2016). Breed

improvement programs undertaken on Pacific white shrimp (P. vannamei) have now

been trialled for decades in a number of countries (Argue et al., 2002; Benzie, 2009;

Castillo-Juárez et al., 2007; Gitterle et al., 2005c; Li et al., 2015) and some have

achieved significant genetic gains for some commercially important traits including

growth rate (Andriantahina et al., 2013b; Gjedrem and Rye, 2018) and disease

resistance (Cock et al., 2017; Cock et al., 2009; Moss et al., 2012a).

Evaluating the relative additive genetic component (heritability, h2) of target traits

is a crucial step in any breed improvement program because these data allow the

selection strategies employed to be optimised. Most published estimates of

heritability for important morphological traits (or stature traits, including body size

and body weight) for Pacific white shrimp to date have been moderate to high

(h2 >0.15) indicating that significant genetic gains are possible via a genetic selection

approach (Argue et al., 2002; Castillo-Juárez et al., 2007; Li et al., 2015). In contrast,

most estimates for fitness traits have been relatively low (h2 <0.15), including for

pond survival (Caballero-Zamora et al., 2015b; Gitterle et al., 2005a; Li et al., 2015;

Zhang et al., 2017), cold temperature tolerance (Li et al., 2015), White Spot

Syndrome Virus (WSSV) disease resistance (Gitterle et al., 2005a), and growth in the

presence of WSSV (Caballero-Zamora et al., 2015b); however TSV resistance (Moss

et al., 2013) and ammonia tolerance (Lu et al., 2017) are exceptions in this regard.

Furthermore, estimates for fatty acid composition suggest only limited additive

genetic variance exists for this trait in the target species (Nolasco-Alzaga et al., 2018).


Pacific white shrimp is currently, one of the top ranking species in terms of value

among traded seafood commodities worldwide (Kumar and Engle, 2016), and it

constitutes an extremely large and growing industry in China, with annual production

there exceeding 1 million tonnes in recent decades (FAO, 2016c). The seed

production component of Pacific white shrimp farming in China however, is

vulnerable as most seed producers depend on imported Specific Pathogen Free (SPF)

lines that often show susceptibility to a number of local pathogens and diseases

(Cock et al., 2017; Cock et al., 2009; Moss et al., 2012a; Thitamadee et al., 2016).

An alternative way to address this problem is to develop broodstock of improved

lines that are naturally adapted to local farm conditions across the target region. In a

previous study, we evaluated Pacific white shrimp domesticated strains from

hatcheries in China for their relative levels of genetic variation, extent of stock

differentiation and genetic relatedness using microsatellite markers and produced a

complete diallel cross approach to produce a local synthetic base population for

selection, with goals of maximising genetic variation and maintaining inbreeding

rates at appropriate levels (Ren et al., 2018). Here, we examine quantitative data on

genetic variance for growth in the same domesticated lines in China that potentially

could be captured using the above defined “genotypic approach”. In parallel to this

first goal, our objective was to develop a base population of locally adapted stock to

be used in a future breed improvement program.


3.2.1 Animal Material and Crossing Design

Four strains with three replicated breeding lines of each strain (referred to as:

NA_1; SA_1; Kona; and LA) were sourced from 10 different P. vannamei nauplii


suppliers from three Provinces in China (Fujian, Guangdong and Hainan). The initial

target was to produce 96 full-sib families via a complete 4 × 4 diallel of 16 crosses

with 6 replicates. Strains were defined by their relative degree of genetic

differentiation from each other and their documented breeding history (Ren et al.,

2018). Criteria used for inclusion of an individual breeding line into the program

combined the quality of the stock management practices applied at each respective

hatchery and their rankings for genetic diversity levels identified from microsatellite

markers (Ren et al., 2018). A complete diallel cross design was then applied among

the four P. vannamei ‘best’ strains to form the synthetic base population (Table 3.1).

The mean inbreeding estimate among the 16 crosses used in the diallel cross was

approximately 0 based on a RQG relatedness estimation (Ren et al., 2018) conducted

earlier, resulting from the 98 full-sib families generated via monogamous pairing

(Table 3.1).

Table 3.1 Number of families produced from 16 complete diallel crosses betweenfour Penaeus vannamei strains (NA_1, SA_1, KONA, and LA).

Dam

Sire

NA_1 SA_1 KONA LA

NA_1 7 6 6 4

SA_1 6 8 5 6

KONA 9 6 8 5

LA 3 6 4 9


3.2.2 Broodstock Management

Candidate broodstock of 6 to 8 months in age were collected from the four strains

(12 breeding lines with three breeding lines replicated for each strain) and stock

transported to maturation tanks located in Wanning, Hainan Province, China. Each

breeding line was stocked separately into two maturation tanks per line (male and

females kept separately) at a density of 10-15 shrimp/m2. Maturation tanks were

circular polypropylene fibre tanks (3.5 m diameter, 0.9 m depth), with the water

column depth maintained at 50 cm (Figure 3.1a and Figure 3.1b). A biological

recirculating system was used to maintain water quality at an exchange rate of 600%

to 800% per day. The biological filter was constructed of four layers consisting of;

filter biological cotton, silica sand, crushed coral stone, and volcanic rock, with a

total approximate volume of 30% marine water in each tank. During the 3-4 months

pre-maturation stage after arrival, broodstock were fed with a combined diet (2:1) of

commercial pellets (containing 35% to 40% crude protein) and fresh squid, with

water temperature maintained at 22 °C to 27 °C. When all broodstock had reached 10

months of age, females were subjected to unilateral eyestalk ablation. Daily feed

composition consisted of a mixture of fresh meal diet (50% polychaetes, 30% squid

and 20% mussels) at approximately 5% of total biomass per tank. Tank water was

maintained at 28 ± 2 °C and 31-35 ppt salinity.


Figure 3.1 a, b) Maturation tanks system used in the experiment; c, d) selecting

candidate females with ovarian development at IV ~ V stage for artificial

insemination.

3.2.3 Synthesis of Families

Females with ovarian maturation at late IV and V stage (with dark yellow-green

colour ovarian lobes) (Figure 3.1c and Figure 3.1d) were inseminated artificially

using a single male to produce full-sib families. Inseminated females were

transferred to 500 L tanks for spawning. At 1:00 am, berried females were returned

to maturation tanks, and eggs collected using a 25L bucket. Eggs were then

disinfected with iodine (50 ppm) and placed into another 500 L tank (Figure 3.2a)

filled with clean seawater. Gentle aeration was provided to all tanks in the hatchery.

Nauplii (Figure 3.2b) from each family with more than 4,000 individuals were

collected for the next larviculture step (Figure 3.2c and Figure 3.2d). In total, 98

full-sib families were produced over a 21 day period.


Figure 3.2 a) 500L tanks used for families reared separately (with capacity to

produce 245 families each breeding cycle); b) collecting nauplii (cloudy white area)

for the next larviculture step; c) larviculture for families reaching Z2~Z3 stage; d)

family successful reaching PL stage.

3.2.4 Larviculture

Nauplii-5 density in each family was adjusted to approximately 120 individuals/L.

Live microalgae (Chaetoceros sp. and Tetraselmis sp.) were provided to incubation

tanks according to the standard schedule outlined by Treece and Yates (1990).

Freshly hatched Artemia nauplii were prepared for the later protozoeal-3 stage,

following the feeding regime used by Treece and Yates (1990). Commercial


microbound diets (INVE-Frippak, 1CAR, 2CD, 3CD, INVE, Belgium) also served as

a supplement following INVE recommendations. When post-larvae had reached the

PL7-8 stage, 450 individuals from each family were removed and maintained in a

single tank until their body weights had reached 1 to 2 grams prior to family pedigree

tagging.

3.2.5 VIE Tagging

Visible Implant Elastomer (VIE, Northwest Marine Technology) tagging was

used for pedigree identification of full-sib families. A combination of six different

colours (red, green, yellow, purple, blue, and orange) and three different tagging

positions on the body (fifth left and right abdominal segments, and sixth dorsal

abdominal segment) were employed so that each individual could be injected with a

unique family code. A total of 120 individuals from each family were used for VIE

tagging.

3.2.6 Growth Rate and Survival Experiment

Tagged individuals from each family were stocked randomly into 12 recirculating

system tanks (10 m2, the same dimensions as maturation tanks) at a rate of 100

individuals/m2. The feed used in all tanks was a combination of commercial pellets

(35%-40% crude protein) and adult Artemia, with the daily feeding rate fluctuating

between 3%-5% of total biomass per tank. Physical water parameters were

maintained as per the maturation stage for broodstock, except that water temperature

ranged from 22 to 29 °C, depending on both local weather conditions and ocean

water temperature in the hatchery. Stocking density in experimental tanks was

adjusted to 50 individuals/m2 after VIE tagging (body weight (BW1)), following

which this was adjusted to 20 individuals/m2 (sampled at random) when shrimp had

reached 8 months (pre-maturation stage; body weight (BW2)).


On 18th October 2016, the hatchery was hit by typhoon Sarika, interrupting the

electricity supply for 36 hours, resulting in 60% mortality of all experimental shrimp

(after VIE tagging, ~90 days post hatching). Surviving shrimp however, recovered

well (death ratio ~0.01% per day, feeding ratio ~4.5% per day of biomass,

observation of swimming actively) and we estimated that on average ~30 individuals

had survived in all families that could be used to continue the experiment. We

therefore adjusted density by redistributing shrimps among the 12 experimental tanks

equally. The survival trait (S) was defined as the proportion of shrimp alive at BW2

relative to the number alive at BW1. In total, 89 full-sib families data were available

for further data analysis.

3.2.7 Statistical Analysis

The three variables were tested for normality using SPSS. Both BW1 and BW2

were normally distributed, whereas survival was not. As such, we chose to examine

the significance of fixed effects by applying a linear model (GLM) using

UNIVARIATE in SPSS. The variance components and heritability values for

individual traits were estimated using an animal model in WOMBAT (Meyer, 2007)

via restricted maximum likelihood (REML) methodology. The animal model was as

follows:

y = Xβ + Zα + e,

Where:

y is a vector of observations for a trait (body weight: BW1 or BW2; survival:

S (where ( 0=dead, 1=alive));

X refers to a design matrix to fixed effects,


β is the vector of fixed effects including sex, strain effect of dam, strain effect

of sire, strain effect of different combinations, age and tank;

Z is an incidence matrix to animal effects,

α is the vector of random additive genetic effects of the animals, and e is the

random residual. Both a and e follow a normal distribution with mean zero

and variance Aσa2 and Iσe2, respectively. Here, σa2 and σe2 are additive genetic

and error variances and A is the numerator relationship matrix based on

pedigree information.

Total phenotypic variance (σp2) was the sum of additive genetic variance (σα2) and

random residual components (σe2). Heritability was calculated as the ratio of additive

genetic variance to the total phenotype variance (h2= σα2/ σp2).

A multivariate mixed linear animal model was fitted to estimate the genetic

correlation between three traits, expressed in matrix notation as:

yBW1yBW2yS

� ��

(2)

Where yBW1 and yBW2 are body weight at 150 days post hatching and 8 months post

hatching, respectively. yS refers to survival trait between stage between yBW1 and

yBW2. Fixed effects in Model 2 are the same with Model 1. The genetic correlation

between estimated traits (BW1, BW2 and S) were calculated as the covariance of the

standard deviation between two traits as: � � �12

�12 �2

2where σ12 was the calculated

additive genetic covariance between two traits, and σ12 and σ22 were the additive

genetic variances of traits 1 and 2, respectively. Presence of heterosis was assessed

as the percentage change in traits compared with the mean values observed from the


two respective parental purebred strains, significant differences were assessed via

Tukey’s HSD posthoc test in SPSS.

3.3 RESULTS

3.3.1 Survival in Experimental Tanks

Mean survival over 72 days from BW1 (Feb. 9th, 2017) to BW2 (Apr. 12th, 2017)

was 89.1% (Table 3.2). Survival in experimental tanks systems was consistently

high and could be managed, with a cumulative mortality rate per day of 0.15% where

biomass of shrimp per tank ranged from 427.7 to 582.8 g/m2.

Table 3.2 Descriptive statistics for body weight at two different stages (BW1 andBW2) and survival (S)

TraitStructure of data Statistics of data

No. offamilies

No. ofshrimps

Mean no. ofshrimps/family

Mean Minimum Maximum Standarddeviation

BW1 89 2752 30.92 18.65g 1.39g 43.59g 6.74g

BW2 89 2452 27.55 28.52g 4.77g 63.02g 8.69g

S 89 --- --- 89.10% 0 100% 39%

3.3.2 Descriptive Statistics

Details of ANOVA results for individual fixed effects are provided in Table 3.3.

It is clear that all fixed effects impacted the growth and survival traits.


Table 3.3 Summary of analysis of variance for fixed effects (F-statistic value andsignificant level).

Fixed factors DF BW1 BW2 S

Tank 11 91.53 *** 122.52 *** 21.45 ***

Sex 1 N. E. 45.61 *** N. E.

Batch age 10 56.71 *** 83.62 *** 20.66 ***

Strain of dam 3 130.73 *** 172.04 *** 11.26 **

Strain of sire 3 107.20 *** 184.18 *** 2.86 *

Strain

combination

9 60.12 *** 81.32 *** 29.31 ***

The number of recorded families, shrimp and details of the three study traits are

presented in Table 3.2. Mean weight at BW1 across tanks was 18.56 ± 6.74g with

individual weights ranging from 1.39g to 43.59g, while mean weight at BW2 was

28.52 ± 8.69g and individual weights ranged from 4.77g to 63.02g (Table 3.2).

Coefficient of variation for growth was high, but decreased with shrimp age from

36.13% at BW1 to 30.56% at BW2. Coefficient of variation for survival was also

high (28.59%) indicating large differences in mean survival among families.

3.3.3 Effects of strain on growth and survival

Table 3.4 presents differences for three estimated traits among the four purebred

strains and the six crosses. There were no significant differences found between

reciprocal strain crosses (P>0.05) for the studied traits, the data from each bi-

directional cross between pairs of strains were pooled together.


Table 3.4 Estimated means for four purebred strains and six crosses for body weight(g) at two stages (BW1 and BW2) and survival (S %) .

Mating types BW1 BW2 S (%)

N1 means ± SD N2 means ± SD means ± SD

NA_1 × NA_1 111 20.71 ± 7.17 de 62 32.13 ± 7.87 de 0.56 ± 0.05 a

SA_1 × SA_1 219 24.97 ± 7.76 f 212 36.70 ± 9.82 f 0.97 ± 0.01 d

KONA × KONA 382 19.56 ± 5.62 bcd 321 33.86 ± 7.31 ef 0.84 ± 0.02 bc

LA × LA 505 15.03 ± 4.72 a 465 23.27 ± 6.00 a 0.92 ± 0.01 cd

NA_1 × SA_1 80 22.50 ± 6.59 e 68 30.94 ± 8.20 cde 0.85 ± 0.04 bcd

NA_1 × KONA 210 20.01 ± 6.33 cd 168 29.87 ± 8.96 cd 0.80 ± 0.07 b

NA_1 × LA 547 17.43 ± 6.97 b 492 26.33 ± 8.43 b 0.90 ± 0.01 bcd

SA_1 × KONA 152 21.17 ± 4.74 de 147 29.68 ± 6.28 cd 0.97 ± 0.02 cd

SA_1 × LA 173 20.70 ± 5.48 de 149 28.98 ± 6.42 bc 0.86 ± 0.03 bcd

KONA × LA 350 18.41 ± 4.19 bc 343 26.08 ± 5.87 ab 0.98 ± 0.01 d

VIE tag loss 14 --- 25 --- ---

All 2752

18.65 ± 6.74 2452

28.52 ± 8.69 0.89

Specific differences for studied traits among four purebred strains and the sixcrosses were detected via a Tukey’s HSD posthoc test.

N = Number of individuals for data analysis.


From the Table 3.4, it is clear that there were significant mean body weight

differences among strains, with SA_1 (SA_1 x SA_1) always showing the highest

mean body weight at both BW1 and BW2. The LA strain (LA x LA) always showed

the lowest mean body weight at both BW1 and BW2. No significant differences were

evident between the NA_1 and KONA strains at BW1 and BW2. The largest

difference in mean body weight were 66.13% at BW1 (SA_1 vs LA) and 57.71% at

BW2 (SA_1 vs LA). Overall heterosis for BW1 was -0.4%, ranging from -4.9%

(SA_1 × KONA) to 6.5% (KONA × LA). A significant decline (P<0.01) in heterotic

effects was evident in BW2 with overall mean heterosis at -0.08, ranging from -0.16

(SA_1 × KONA) to -0.03 (SA_1 × LA).

Strain SA_1 also showed the highest survival rate, with a mean survival rate of

97%. In contrast, strain NA_1 was ranked lowest for survival rate, with a mean of

56%. The LA and KONA strains were intermediate for mean survival rate, with

means of 92% and 84%, respectively. Thus, the largest difference in overall mean

survival rate was as much as 73% (SA_1 vs NA_1). Overall mean heterosis of S was

0.04 ranging from -9.0% (SA_1 × LA) to 21.6% (NA_1 × LA), a significant

difference compared with that observed in purebred strains (P<0.01).

3.3.3 Genetic Analysis of Growth and Survival Traits

Estimates of variance components, heritability and genetic correlations for growth

traits (at BW1 and BW2) and survival (S) are presented in Table 3.5. Heritability for

growth rate was high (h2>0.4), with 0.52 ± 0.09 at BW1 and 0.44 ± 0.07 at BW2,

respectively. In contrast, h2 for survival (S) was low (0.01 ± 0.02) and was not

significantly different from zero indicating only very limited additive genetic

variance for this trait. The genetic correlation between BW1 and BW2 was moderate


(0.95 ± 0.03) and was also significantly different from zero. Genetic correlations

between survival (S) and growth (BW1 and BW2) however, were low at 0.26 ± 0.51

(S and BW1) and 0.18 ± 0.01 (S and BW2), respectively and both estimates were not

significantly different from zero (p>0.05).

Table 3.5 Estimates of variance components (σ2p, the phenotypic variance; σ2a, theadditive genetic variance; σ2e, the random residual error variance), heritabilities (h2,ratio of additive genetic variance; e2, ratio of random residual error variance), andgenetic correlations for the body weight (BW1 and BW2) and survival (S) traitsbased on univariate animal model analysis.

*Estimate is highly significantly different from zero (P<0.01).NS Estimate is not significantly different from zero (P>0.05).

3.4 DISCUSSION

As far as we are aware, the current study is the first report of a penaeid shrimp

breeding program employing domesticated strains based on an experimental design

that applies a “genotype approach” to initiate the genetic selection program.

Heritability for body weight at two ages (BW1, h2=0.52; BW2, h2=0.44) were quite

high with quantitative data available from 89 full families and approximately 30

individuals per family providing strong evidence that broad genetic variation for

TraitsVariance components Heritability (±SE) Genetic correlation (±SE)

σ2p σ2a σ2e h2 e2 BW1 BW2

BW1 43.27 22.54 20.73 0.52 ± 0.09 0.48 ± 0.07

BW2 47.46 20.69 26.77 0.44 ± 0.07 0.56 ± 0.07 0.95 ± 0.03*

S 0.15 0.00 0.15 0.01 ± 0.02 0.99 ± 0.01 0.26 ± 0.51NS 0.18 ± 0.01 NS


growth rate was available in the base population developed for the project. This is

evident even though we employed a monogamous mating design where we could

only estimate genetic variance of family differences, an approach that has potential to

overestimate heritability as a result of maternal or random effects common to full-

sibs. From this base and with appropriate stock management and applying the

breeding strategies rigorously to preserve maximum levels of genetic variation

within families and by controlling inbreeding, we are confident that future genetic

gains can be optimised and that the planned selection to produce a fast growth stock

will be successful.

3.4.1 Experimental Tank System

A major constraint on penaeid shrimp breeding in the past has been designing and

building reliable, high quality environmental closed water systems for domesticated

stock in captivity (Coman et al., 2005; Duy et al., 2012; Yano, 2000). Traditional

maturation or indoor culture systems that have employed flow-through water systems

have regularly experienced average mortality rates of ~0.5% per day. This rate is in

general, too high to support efficient shrimp genetic improvement programs when we

consider mean age at maturation (commonly >10 months) vs mortality rate (~0.5%

per day) and the bio-secure environmental conditions required to produce SPF

broodstock. In our experimental tanks, except for the unpredictable impacts of a

typhoon, water conditions were highly suitable for an improvement program for

domesticated shrimp as is evident in the survival data where average mortality rate

per day was less than 0.1% from VIE tagging age through to BW1 data collection

(data not provided), followed by an average mortality rate of only 0.15% per day

during the pre-maturation stage from BW1 to BW2. Biomass reared in our

experimental tanks ranged between 427.71 to 582.76 g/m2 and this already meets


basic requirements for broodstcok maturation to produce nauplii successfully in most

commercial hatcheries in China maintaining a stocking density of Pacific white

shrimp of size 40-60g at approximately 6-8 individuals/m2.

3.4.2 Genetic Parameters for Growth and Survival Traits

In the present study, heritability estimates for growth rate (BW1 and BW2) were

high indicating that large genetic variation was present at both developmental stages

tested for this trait in our base population. In general, our estimates were consistent

with results of reviews of heritability estimates for body size (or stature) across a

wide range of species (from model species like Drosophila, wild animals,

domesticated species to humans) that is generally moderate to high with h2 estimates

ranging from 0.15 to 0.85 (Visscher et al., 2008). Estimation of heritability in a

specific population however, will depend on partitioning observed phenotypic

variation into unobserved additive genetic variance and environmental factors, thus

heritability will vary under different environmental (culture) conditions (Hill, 2014;

Visscher et al., 2008). Studies on genetic breeding programs for penaeid shrimps that

have examined environmental effects on heritability estimates for growth rate have

identified three factors that can influence the estimate (a) sub-optimal environmental

conditions (including suboptimal low water temperature, the presence of disease,

pathogens, etc.); (b) normal commercial farming conditions; and (c) the ability to

maintain optimum environmental conditions in recirculating water tank systems or

clear water systems. Our results are consistent with the last defined group (c), where

h2 ranged from 0.23 to 0.84 with a mean >0.4 (Argue et al., 2002; Coman et al., 2010;

Kenway et al., 2006; Macbeth et al., 2007). Heritability estimates in the current study

were higher than reported for group b (most estimates for h2 were between 0.15 to


0.4) (Campos-Montes et al., 2013; Campos-Montes et al., 2017; Castillo-Juárez et al.,

2007; Gitterle et al., 2005c; Ibarra and Famula, 2008b; Krishna et al., 2011; Nolasco-

Alzaga et al., 2018; Pérez‐Rostro and Ibarra, 2003a; Sui et al., 2016a; Sui et al.,

2016b; Sun et al., 2015a; Zhang et al., 2017). Heritability estimates for growth were

also much lower in sub-optimal culture conditions with h2 = 0.09 in the presence of

WSSV disease vs 0.15 in a culture pond without WSSV present (Caballero-Zamora

et al., 2015b), and 0.30 in under low and suboptimal water temperature conditions vs

0.48 in controlled water temperature culture ponds (Li et al., 2015).

It is widely recognised that heritability estimates for growth rate can also change

with age class. Our estimates for h2 at the sub-adult BW1 stage (~24 weeks) were

marginally higher than that at BW2 the maturation stage. In general, it is expected

that heritability for growth should increase across the growth period. In Giant

freshwater prawn (M. rosenbergii), heritability h2 estimates for body weight

increased from pre-market to market size, from 0.11 to 0.15 (Hung et al., 2014).

Similar patterns have also been reported in Pacific white shrimp (Campos-Montes et

al., 2013; Pérez‐Rostro and Ibarra, 2003b; Zhang et al., 2017). In the current study in

contrast, we observed higher heritability estimates for growth at the earlier life stage

(BW1) examined. This pattern has also been reported in some other penaeid shrimp

breeding programs in particular ones applied to P. monodon stock improvement

(Coman et al., 2010; Kenway et al., 2006; Macbeth et al., 2007).

Knowledge about genetic parameters for traits at different developmental ages and

associated genetic correlations among traits can allow earlier application of selection

to achieve genetic gains later in the growth cycle or selection of a mixed age cohort

to maximise genetic gains (Campos-Montes et al., 2013; Hung et al., 2014).

Moreover, applying selection early during the growth phase can reduce costs


associated with maintenance of facilities in the hatchery and/or grow out systems. In

penaeid shrimp breeding however, it is often necessary to delay initiating selection

because the larval nursery phase is extended delaying the stage at which physical

tagging of families can take place (60-80 days to reach body size ~1.5g) and

estimating heritability during the nursery phase potentially may be confounded by

maternal and/or common environmental effects in family tanks (Montaldo et al.,

2013). Moreover, where heritability of growth traits has been estimated at early

stages during development in shrimp taxa, estimates have been quite low (i.e.

h2<0.15) (Campos-Montes et al., 2013; Hung et al., 2014), which if applied in

models would suggest only limited genetic gains were possible.

h2 estimates for survival were low in our study, a result that is consistent with

many previous studies of cultured shrimp species, including for P. vannamei

(Gitterle et al., 2005c; Li et al., 2015; Zhang et al., 2017) and M. rosenbergii (Luan et

al., 2015). Estimates of heritability for survival in aquatic species in general however,

vary widely. Tan et al., (2017a) reported back-transformed h2 estimates for survival

in P. vannamei were 0.36 for low and 0.22 for high density treatments, respectively.

In red tilapia, h2 estimates for survival ranged from 0.02 to 0.68 and were affected by

both test environments and the data analysis methods employed (Nguyen et al.,

2017). In general, survival is a fitness trait that can be affected by many factors.

These factors will include reflecting the relative health fitness status of individuals

that is essentially resistance to, and/or tolerance of, multiple and potentially unknown

factors and such traits usually have low heritability (Falconer and Mackay, 1996;

Sae-Lim et al., 2013). Estimated heritability for survival (S) in our study as with

many earlier studies, suggests that there is very limited potential to improve survival


rate using a directional selection approach. Alternatively, developing disease-

resistant strains could offer an indirect way of providing the opportunity to improve

this important trait and in this regard, there has been significant recent progress with

respect to improving survival in shrimp farming (Cuéllar-Anjel et al., 2012; Moss et

al., 2013).

3.4.3 Genetic Correlations between Growth and Survival

The genetic correlation between growth estimates at different ages in our study

was high (0.95), indicating selecting for body weight at the younger age will improve

growth rate at the later age simultaneously. This result accords well with other

estimates for penaeid shrimp species in the literature. Reported genetic correlations

for growth traits at different ages have ranged from 0.71 to 0.95 in P. vannamei

(Campos-Montes et al., 2013; Pérez‐Rostro and Ibarra, 2003b; Zhang et al., 2017)

and 0.63 ± 0.19 in P. monodon for body weight at 16 weeks and 24 weeks (Coman et

al., 2010), respectively. Recognition of this association can be used to optimize

selection for P. vannamei in commercial breed improvement programs. Improving

growth rate is the most important commercial trait targeted in breeding of farmed

aquatic species, and the majority of earlier programs that reported their genetic gains

from selection work refer to growth rate as the only trait targeted (Gjedrem and Rye,

2018). Growth can be measured by estimating body weight at specific ages, from

morphological traits (e.g. total length, body length, etc.) or by sampling daily growth

rate. Body weight and specific morphological traits are most useful due to the

practicality of data measurement, and the ease of collecting accurate data.

Furthermore, recognition of high phenotypic and genetic correlations between body

weight and other morphological growth traits at specific ages (i.e. rp>0.95; rg>0.95),

means that we can often select based on measurements of only a single trait for


growth, thereby saving significant time and cost (Campos-Montes et al., 2017;

Krishna et al., 2011; Pérez‐Rostro and Ibarra, 2003b; Sun et al., 2015a).

In our study, we consider BW1 represents the most appropriate time to apply

selection in real commercial programs due to high heritability and mean body size

closest to real harvest size in shrimp farming as most farmers harvest their shrimps at

~20 g. Another advantage in penaeid shrimps of choosing this younger age is to

avoid later impacts of heterogeneous growth related to sex (females usually mature at

much larger size than do males). In P. vannamei however, both sexes have similar

growth rates before body size reaches 25 g. So by employing selection at a mean

weight of ~20g this essentially eliminates any gender effect on estimates of genetic

parameters or Estimated Breeding Values (EBVs).

The genetic correlation between survival rate and growth rate was low and not

significantly different from zero in our study. While this result accords with some

previous reports on penaeid shrimps, others have reported both positive (Gitterle et

al., 2005c; Zhang et al., 2017) and negative (Kenway et al., 2006; Zhang et al., 2017)

associations between the two traits.

3.4.4 Effects of Strain on Growth and Survival

This is the first report of differences in growth and survival rate in domesticated P.

vannamei strains in China. Clearly, the SA_1 strain showed a superior growth rate

while LA showed the lowest growth rate. To our knowledge, differences in growth

rates among strains observed here are consistent with anecdotal evidence from the

shrimp farming industry in China. In the current study, variation in mean body

weight among strain combinations was much higher than that seen with other aquatic

species subject to diallel crossing, including common carp (Nielsen et al., 2010) and


European sea bass (Vandeputte et al., 2014). This probably results from the

significantly different genetic backgrounds of individual strains (Ren et al., 2018),

the different breeding strategies applied to strains before they were introduced into

China, and to a some extent, the different management practices employed on strains

in various hatcheries in China following their introductions. While no significant

positive heterosis was evident for body weight among the different crosses, the result

is consistent with other aquatic species (Bentsen et al., 1998; Gjerde and Refstie,

1984).

NA_1 in the current study showed a significantly lower survival rate compared

with the other strains examined. This reflects the significant reduction of available

genetic resources for NA_1 since introduction to China in 2014, possibly related to

the issue of low pond survival. Prior to this, NA_1 made a major contribution to P.

vannamei broodstock production in China. High inbreeding rates in recent times

however, has probably contributed to the low survival rate of this strain seen

currently. In our earlier study, we showed that NA_1 culture resources in China

generally showed comparatively low genetic diversity levels based on microsatellite

markers (Ren et al., 2018). High inbreeding levels commonly impact negatively on

fitness traits related to survival and growth rates. Survival rate is however,

significantly impacted by performance in different culture environments due to G X

E effects. Therefore, the strain differences in survival rate detected here may not

necessarily be a reflection of any specific commercial farming environment.

3.4.5 Implications for Further Study

The main objective of the current study was to initiate a base breeding line for

Pacific white shrimp in China and to exploit additive genetic variance in locally

adapted culture lines. Since the sample size of most purebred strains or crosses


compared here was less than 15 replicates, we could not investigate potential

heritability values for 10 individual crosses of mating type. To date, there has only

been a single program in penaeid shrimps that has directly exploited heterosis

between strains to improve culture performance (Goyard et al., 2008a). Exploiting

additive genetic variation however, could also benefit the shrimp farming industry in

China over the long term. Therefore, we recommend that initial selection be

undertaken within strains (Ponzoni et al., 2013) to homogenize growth performance

in early generations to conserve more genetic variation and to achieve genetic gains

over longer time frames.

While the current study employed recirculating water tanks as culture

environments to estimate impact of family selection on growth rate, future

application of an improved line under commercial farming environmental conditions

will require genotype-by-environment (G×E) interactions to be assessed. After

reviewing studies of G×E interactions on growth traits in 38 aquatic species, average

genetic correlation was reported to be 0.72 (Sae‐Lim et al., 2016) and was even

higher in earlier experiments conducted on penaeid shrimps for growth performance

in different culture environments (Gitterle et al., 2005c; Ibarra and Famula, 2008b;

Tan et al., 2017a). This suggests that the base line developed in our study has a high

chance of also being successful after selective breeding for fast growth traits in a

variety of different shrimp farming environments.

Our test tanks provided a reliable and excellent clean water system for

domestication of P. vannamei. At the BW2 data collection stage, mean body weight

for the top 30% of female and male broodstock were 41.22 ± 5.26g and 37.05 ±

4.59g respectively, a mean size that in general, accords with maturation size of SPF


stocks used to produce next generation families for genetic improvement work.

Furthermore, our experimental tank system can also be used for maturation and

meets bio-secure SPF environmental requirements as a nucleus-breeding centre

(NBC) for cultured SPF P. vannamei broodstock according to current quarantine

regulations in China.


3.5 CONCLUSIONS

In conclusion, our study shows that there is substantial genetic variation for

growth traits in the L. vannamei synthetic breeding line established in our study in

China. Synthesising a base population using domesticated Chinese culture strains

from multiple diverse source populations captured broad genetic variation for rate of

growth that can be exploited in a future breed improvement program. In the future, to

improve the accuracy of estimating genetic parameters, random additive effects

should exclude maternal effects and random environmental effects common to sibs.

These goals can be achieved by a nested mating design and via communal rearing of

each family shortly after hatching and subsequently rebuilding pedigree information

from molecular marker data.

107 Chapter 4: Comparison of Reproductive Performance of Domesticated P. vannamei FemalesReared in Recirculating Tanks and Earthen Ponds: An Evaluation of Reproductive Quality of Spawnsin Relation to Female Body Size and Spawning Order

Chapter 4: Comparison of Reproductive Performance of

Domesticated P. vannamei Females Reared in Recirculating Tanks and

Earthen Ponds: An Evaluation of Reproductive Quality of Spawns in

Relation to Female Body Size and Spawning Order


ABSTRACT

Based on estimated levels of heritability for growth traits in our nucleus

population, we had determined that appropriate levels of genetic variation were

available in our line to exploit fast growth traits. The next step in the genetic

improvement program therefore, was to optimize the quality of reproductive

performance of candidate broodstock, and to progress plans for the long term

conservation of genetic variation in the nucleus population, facilitating the

development of an effective seed dissemination strategy. Here we investigated the

relative reproductive performance of female broodstock reared under two common

rearing systems: i) recirculating tanks (RT) and ii) earthen ponds (EP), and evaluated

the relative quality of individual reproductive performance (RT vs EP), the quality of

reproductive females in relation to individual body size of spawners, and female

reproductive quality relative to spawning order. For this analysis, broodstock under

two culture conditions (RT and EP) using nauplii produced from spawning of a

single batch to eliminate any potential effects from the genetic resources used or age.

were sourced a single night’s cohort of nauplii, with the aim of eliminating any

potential impacts from genetic resource or age. Individuals were reared as

broodstock in either RT or EP rearing systems for the experiment. In total, we scored

reproductive parameters for 156 spawning females (107 RT-reared females and 49

EP-reared females) in two replicate maturation tanks over a 30 day test period in the

second month after unilateral eyestalk ablation. No significant difference (P>0.05)

was observed between RT-reared females and EP-reared females for number of eggs

per spawn (RT= 23.34 ± 0.72 × 104, EP= 22.45 ± 0.67 × 104), number of nauplii per

spawn (RT= 19.85 ± 0.85 × 104, EP= 19.53 ± 0.83 × 104), hatch rate of eggs per


spawn (RT= 0.83 ± 0.02, EP = 0.85 ± 0.02) and relative fecundity - number of eggs

per g of female (RT= 5.51 ± 0.15× 103, EP= 5.78 ± 0.19 × 103). We recorded 136

and 101 spawning events for RT and ER females, respectively. EP-reared females

(1.93 ± 0.23) showed a significantly higher (P<0.01) spawn frequency compared

with RT-reared females (1.34 ± 0.12). Females under the two treatments showed a

similar pattern for larger body size spawners producing higher numbers of eggs and

nauplii per spawn. Of interest, we observed that while large sized RT-reared females

recorded a higher mean spawn frequency, medium-sized females showed double the

spawn frequency compared with small or large sized females in the EP treatment. No

evidence observed for quality of individual female reproductive performance for

multiple spawning individuals compared with first or second spawning only

individuals for all reproductive parameters evaluated (P>0.05).

Keywords: Reproductive performance, Broodstock, Penaeus vannamei, Multiple

spawning


4.1 INTRODUCTION

In most breed improvement programs for farmed aquatic species, the next stage

after development of a breeding nucleus is to identify the best breeding candidates

and to optimise their reproductive performance prior to implementing selection

(Gjedrem and Thodesen, 2005). In penaeid shrimp breeding, specific tasks involved

in this step include: (a) rearing offspring of individuals from the nucleus to sexual

maturation; (b) providing the best broodstock to multipliers; and (c) supplying

nauplii or post larvae (usually PL5 or PL10) to the nursery sector - or if a nursery

stage is not included then juvenile shrimp are supplied directly to growout farmers.

In part, this sequence requires that broodstock used to produce juveniles show good

individual reproductive performance as this is essential, not only to preserve genetic

resources in the nucleus and to accumulate optimal breeding traits in live animals

across generations, but also facilitates dissemination of quality seed to the growout

industry. This is because the majority of profit generated can then be fed back into

investment in the breeding program. From the perspective of a seed multiplier,

important parameters determining relative individual female reproductive quality

include; the number of eggs per spawn (NE), the number of nauplii per spawn (NN),

the hatch rate of eggs (HR) and the proportion of females in the broodstock

population that spawn per night (this also equates to spawn frequency (SF) of

females), total nauplii numbers produced and the associated profit that is possible.

While just about all broodstock used currently in P. vannamei farming around the

world are sourced from captive domesticated spawners (Andriantahina et al., 2012b;

Benzie, 2009; Ceballos-Vázquez et al., 2010; Ibarra et al., 2007b), this is not true for

most other farmed penaeid species (i.e. P. monodon, P. japonica, P. paulensis, P.


indicus) that still rely fully or partly on broodstock sourced from wild populations

(Arnold et al., 2013; Boucard et al., 2004; Jiang et al., 2009; Marsden et al., 2013;

Peixoto et al., 2011; Peixoto et al., 2008; Preston et al., 2004). Use of domesticated

stocks maintained under appropriate bio-secure control conditions (that may include

specific pathogen-free (SPF) management) can effectively address most problems

associated with employing wild broodstock (Cock et al., 2017; Cock et al., 2009;

Gjedrem and Rye, 2018). Moreover, availability of fully domesticated spawners can

be more economic compared with the costs associated with collecting wild stock,

their year-round availability, and relative quality of their performance. Recently, the

availability of fully domesticated culture lines of Pacific white shrimp in the Asia-

Pacific region has led to significant growth in production of farmed prawn there

(Kumar and Engle, 2016; Lightner et al., 2009b).

Relative reproductive performance of domesticated stock however, needs to be

evaluated appropriately before broodstock are released to the seed production sector.

There has been significant controversy about the relative reproductive performance

of domesticated lines over the past 40 years, resulting from a range of factors

including impact of age, size and/or genetic background (Aquacop, 1979; Arcos et al.,

2005a; Arnold et al., 2013; Browdy, 1998; Coman et al., 2006; Marsden et al., 2013;

Medina et al., 1996; Menasveta et al., 1993; Peixoto et al., 2008; Preston et al., 1999;

Primavera and Posadas, 1981; Wen et al., 2015).

Female reproductive performance in penaeids can be impacted by a number of

factors including: individual physical status, environmental culture water factors,

nutrition and additive genetic composition (Benzie, 1997; Ibarra et al., 2007b). The

same stock grown in different culture conditions can also show different reproductive

performance due to both effects of varying environments and differences in


nutritional factors. Well managed recirculating tank (RT) systems provide a stable

high quality water environment in indoor bio-secure conditions that should result in

lower mortality and minimum water pollution. For these reasons, they have been

considered to be an ideal rearing system for closing the life cycle of penaeid shrimp

in genetic improvement programs, and also for producing mature SPF broodstock for

the industry (Chen et al., 1991; Crocos and Coman, 1997; Duy et al., 2012; Otoshi et

al., 2003). Tank-reared broodstock however, often do not show comparable

reproductive performance compared with stocks reared in earthen ponds or even that

of wild populations (Andriantahina et al., 2012b; Arnold et al., 2013; Coman et al.,

2006; Otoshi et al., 2003). This issue needs to be further investigated to help meet

demands of the seed production sector.

Earthen ponds (EP) are widely used for rearing domesticated P. vannamei stocks

in the shrimp farming industry (Briggs et al., 2004). Small entrepreneurial family

holders in China first learned about maturation of P. vannamei broodstock in earthen

ponds after unilateral eyestalk ablation was introduced by a Taiwanese technician in

the late 20th century (pers. comm., Aibing Gao, President of the Pacific white Shrimp

Seed Association of Xiamen), and this development pioneered shrimp farming in

China. For decades, this method of nauplii production has contributed more than

50% to total nauplii supply in the seed sector in China. This practice is now used

widely in China where, an annual production of more than one million tons of Pacific

white shrimp has been produced for decades (FAO, 2016c). Several anecdotal stories

about nauplii production in earthen ponds however, have indicated that problems still

exist. Farmers using this system prefer small and medium sized female broodstock

rather than choosing large individuals because they consider that smaller-sized


mature females show better reproductive performance. Results of scientific studies

on the relationship between body size and individual reproductive performance in

penaeid shrimps in contrast, have suggested the reverse relationship (Andriantahina

et al., 2012b; Arcos et al., 2003a; Arnold et al., 2013; Ceballos-Vázquez et al., 2010;

Ibarra et al., 2007b; Peixoto et al., 2003). Moreover, technicians running hatcheries

often claim that stocks raised in earthen ponds are easier to bring to maturity and

show a higher mating rate compared with imported SPF stocks (generally reared in

recirculating tanks (Otoshi et al., 2003)). To date, there have only been two

comparative studies on relative reproductive performance of P. vannamei broodstock

reared in RT vs EP systems (Andriantahina et al., 2012; Otoshi et al., 2003).

Estimated reproductive parameters were also collected following artificial

insemination that produced significantly low NE, NN, and HR rates compared with

natural mating designs and data available from current commercial nauplii

production.

External body size is the principal criterion for selecting female broodstock in

penaeid shrimps because it is non-invasive and easy to measure relative to the labour

and costs involved (Arcos et al., 2003a; Ibarra et al., 2007b). In general, large female

penaeids are considered better quality spawners because there is evidence for a

positive correlation between individual size and fecundity (NE) (Arcos et al., 2003a;

Emmerson, 1980; Ottogalli et al., 1988; Palacios et al., 1998) and spawn frequency

(SF) (Arnold et al., 2013; Hansford and Marsden, 1995; Menasveta et al., 1994;

Palacios et al., 2000; Wen et al., 2015). For choice of female P. vananmei broodstock,

the recommendation is to use 30 to 45 g individuals (Aquacop, 1983; Bray and

Lawrence, 1991; Otoshi et al., 2003; Wyban and Sweeney, 1991). In small family

hatcheries in China however, farmers usually select females of 25-35g for nauplii


production. Choice of individual body size of broodstock can be related however, to

the rearing systems used due to effects of culture density, physical environmental

factors in the water used, and nutritional conditions. As a result, no clear advice is

currently available for new producers, so selection of broodstock based on individual

size requires further investigation to determine impacts of rearing systems. In

addition, results of earlier studies that investigated the relationship between body size

and reproductive parameters have varied widely (in some cases they show

contrasting results) in particular, in terms of hatch rate of eggs (HR). Of interest

however, is that most experimental tests of the above parameters have produced

estimates much lower than is achieved currently under commercial production

conditions. This highlights a need to standardize broodstock maturation

environments and nutrition. A starting point for this is to develop optimal

management in experimental test tanks and then later, to trial the procedures at larger

scales.

The primary objective to optimizing quality of larval production in penaeid

shrimp culture is to understand the mechanism(s) behind why a large proportion of

mature females reproduce infrequently or may never spawn, while at the same time

only a very small proportion of mature females spawn multiple times and hence

contribute the majority of nauplii in a hatchery (see reviews by (Arcos et al., 2003a;

Ibarra et al., 2007b)). For more than 40 years, studies have tried to manipulate a

variety of factors to improve the rate of multiple spawning in penaeid species.

Factors that have been considered include: phenotypic traits (Arcos et al., 2003a;

Hoang et al., 2002; Menasveta et al., 1994; Palacios and Racotta, 2003; Palacios et

al., 1999a); physiology and biochemistry (Arcos et al., 2003b; Palacios and Racotta,


2003; Peixoto et al., 2004); nutrition (Coman et al., 2007; Goodall et al., 2016; Hoa

et al., 2009); additive genetic components (Arcos et al., 2005b; Ibarra et al., 2009;

Macbeth et al., 2007); hormonal levels and functional gene expression (Huerlimann

et al., 2018; Treerattrakool et al., 2014; Tsutsui et al., 2005). To date however, no

studies have fully addressed this question or provided practical ways to improve or

optimize spawning frequency. For P. vannamei, the two culture systems (RT and EP)

widely used for domesticated stocks, may potentially impact spawning frequency. To

date however, no studies have investigated the impact of culturing shrimp in RT vs

EP environments on spawning frequency in P. vannamei stocks.

While multiple spawning of individual mature females is considered to be a

desirable trait to improve overall hatchery reproductive capacity, it is widely agreed

that there should be no compromise made on offspring quality when considering this

trait. Producers have also suggested that the egg quality of multiple spawners should

not necessarily deteriorate from the first spawn (Arcos et al., 2004; Ibarra et al.,

2007b; Palacios and Racotta, 2003), but they acknowledge that time factors have

always potentially impacted the quality of their comparisons. Reproductive

exhaustion of broodstock individuals during nauplii production in penaeid shrimps is

however, recognised to be a relatively common phenomenon (Palacios et al., 1998;

Palacios et al., 1999b; Wyban, 1997), in particular when maturation conditions have

not been managed appropriately. Again, this highlights that it is important to

consider how optimal the maturation conditions employed have been during any

experimental tests of female reproductive quality.

In the current study we reared P. vannamei broodstock under two culture

conditions (RT and EP) using nauplii produced from spawning of a single batch to

eliminate any potential effects from the genetic resources used or age, and compared


(a) differences in individual mature female reproductive performance under RT and

EP culture treatments, (b) the relationship between females size and the quality of

their reproductive parameters, and (c) female reproductive quality relative to

spawning order, under optimal maturation test environment conditions. Results

generated here can be applied to developing improved genetic management of

cultured P. vannamei lines and to optimise seed production for the shrimp farming

industry in China.

4.2 METHODS AND MATERIALS

4.2.1 Experimental Animal

Shrimp nauplii used in the study came from a single mass spawning (~ 80 full

families) on a single night in a commercial hatchery owned by the Beijing Shuishiji

Biotech Ltd. at Wanning, Hainan Province, China. Larval culture and the nursery

phase occurred from 1st July to 20th July 2017, using identical procedures as

described in Chapter 3. Post larvae (PL10 stage) were sampled randomly and

transferred to either EP or RT for growout.

4.2.2 Broodstock Rearing Procedure in Earthen Ponds

Individuals were stocked into 0.8 ha earthen ponds (Figure 4.1a) in a commercial

shrimp farm owned by Beijing Shuishiji Biotech Ltd. at Wanning, Hainan. Initially,

PLs were stocked at a density of 25 individuals per m2 and fed with a commercially

formulated diet (EVERGREEN AQUATIC & Ltd.) containing 40% dietary crude

protein. Feeding ratio over the first five months of the growout stage was

approximately 10% biomass initially, a level that was decreased at a steady rate to

2% of biomass by the end of the cycle. At the end of the five month culture period,


shrimp were collected (Figure 4.1b) at random and transferred to another earthen

pond and supplied with enhancement nutrition for three months to reach pre-

maturation stage. Management and feeding strategies during this time was almost

identical to that of the first growout stage, except that shrimp were also supplied with

fresh squid meal twice per week.

Figure 4.1 a) Earthen ponds for experimental broodstock trials; b) shrimp after five

month culture period (size of 20.0 ~ 25g); c) shrimp at eight months; d) packaged

broodstock in 10 L nylon bags (temperature at ~ 18 °C ) and transferred to hatchery

for reproductive traits test.

4.2.3 Broodstock Rearing Procedure in Recirculating Tanks

Shrimp were stocked into RT using the same standard procedure used in the

family growth parameter study as described in Chapter 3.


4.2.4 Design for Experimental Comparisons

When individuals had reached eight months of age (Figure 4.1c), broodstock

from EP/RT were collected at random and transferred (Figure 4.1d) to the hatchery

for acclimation in maturation tanks. Trials used four x 10 m2 RT with two replicates

per treatment. Mature males and females were reared separately at a stocking rate of

eight individuals per m2. Broodstock maturation management was the same as that

employed earlier (Chapter 3). Mature females from EP and RT were tagged with

individual numbered silicon eye rings (Starfish & Ltd.) for source identification and

then mixed together for the experiment. At 10 months of age, test females were

subjected to unilateral eyestalk ablation (Figure 4.2). Reproductive parameters for

females in both the RT and EP treatments were collected one month after eyestalk

ablation, and data recorded for 30 days. Females with mature ovaries (stage IV) were

collected daily at 10:00 AM and transferred to tanks with mature male broodstock.

At 19:00 PM, successfully mated females with attached spermatophores were placed

into individual 500L fibreglass tanks filled with 300L clean seawater. Spawning

environmental conditions were maintained at 28 ± 0.5 °C and a salinity of 32-36 ppt.

At 24:00, all females in the spawning tanks where eggs were released, were then

returned to their maturation tank. Eggs incubated with gentle aeration supplied. In

total, 107 RT females and 49 EP females broodstock were used for the estimation of

individual reproductive parameters.


Figure 4.2 Test females subjected to unilateral eyestalk ablation.

4.2.5 Evaluation of Reproductive Parameters

Reproductive performance was assessed for a series of standardized parameters

over 30 days. Survival of females under the two treatments (RT vs EP) was recorded

over the one month experimental period. After successful spawning, body weight

after spawning (BW) was estimated using a scale. Individual female fecundity was

measured using two methods; first, number of eggs (NE) per spawn was calculated

using a 200 ml beaker sub-sampling method with three replicates after eggs were

thoroughly homogenized in the spawning tanks with 300 L volume seawater.

Secondly, relative fecundity (FE) data were estimated as the number of eggs per unit

body weight after spawning. The number of nauplii per spawn (NN) was measured

using the approach as used for NE on the second day at 11:30 AM after nauplii had

hatched. Hatchability was measured as the percentage of hatching nauplii per

spawning event as (NN/NE) × 100%. Spawning frequency (SF) of each female was

examined after one month when the experiment had ended. Finally, the number of


successful spawning events was recorded for each surviving broodstock female at the

end of the experiment.


Percentage data for HR were arcsine transformed prior to analysis (Zar, 1996).

Both percentage data (HR) and back-transformed data (HRat) were used and included

in further statistical analyses. Single factor one-way ANOVAs were performed to

compare reproductive parameters between treatments (RT vs EP). Variables

evaluated included: BW, NE, NN, HR, HRat, FE and SF. In addition, the interaction

between spawning frequency and rearing environment were investigated using a chi-

square test of independence. To evaluate the effect of body size in relation to

reproductive parameters, BW was first divided into three individual female size

classes namely: small (BW<38 g), medium (38-48 g), and large (>48 g) sized

individuals (the threshold for the three size categories was based on criteria of body

size for broodstock selection (see Introduction), with the objective to introduce a

variance for body weight of statistically significant differences among three sized

group within the two treatments.). Following this, BW group data were introduced as

a covariate in an ANCOVA analysis. To assess the quality of reproductive

performance in relation to ‘spawning order’, spawning events were also divided into

three groups, namely: first spawning event over the experimental period, second

spawning event over the experimental period, and multiple spawning events (three or

more) over the experimental period. The three groups for ‘spawning order’ were set

as a covariate in the ANCOVA analysis. Tukey’s post hoc means comparison was

used to assess significance differences between means after ANOVA analyses. Level


of statistical significance was set at P<0.05. All statistical analysis were performed

using SPSS 23 (IBM).

4.3 RESULTS

4.3.1 Reproductive Performance in Relation to Treatment (RT vs EP)

Means for reproductive parameters from broodstock females reared in the two

culture environments (RT and EP) over a one month test period are presented in

Table 4.1. No statistically significant difference was evident for female survival rate

between the two culture environments, 94% - RT vs 92% - EP, respectively. 136

spawning events for females were recorded in the RT treatment vs 101 spawning

events in the EP environment. In general, reproductive quality parameters namely;

mean number of eggs per spawn (NE), mean number of nauplii per spawn (NN),

mean hatchability rate of eggs per spawning event (HR), mean hatch rate per

spawning event number after arcsine transformation (HRat) and mean fecundity of

each spawning event (FE) were all not significantly different between treatments.

Mean values for NE, NN and HR for females from both RT or EP treatments were

within the range for optimal commercial nauplii production in China (pers. obs.).

Females in the EP treatment however, showed significantly higher spawning

frequency than females in the RT treatment (P<0.01).


Table 4.1 Comparison of reproductive performance (plus standard errors) of P.

vannamei broodstock reared in two different treatments: earthen ponds (EP) vs

recirculating tanks (RT). Bold type indicates a significant difference (p<0.05).

Of the 101 females in the RT treatment over the 30 day experimental trial,

approximately 30% of individuals did not spawn, while another 30% spawned only

once. 23% spawned twice and 17% spawned three or more times (Figure 4.1 a). In

the EP treatment, 20%, 30% and 14% of females did not spawn, spawned once, or

spawned twice, respectively. The proportion however, of multiple spawners (38%) in

the EP treatment was significantly higher (χ2(3, 0.05), 8.392, P = 0.039) than that in the

RT treatment (Figure 4.1 b).

Reproductive parameters

Broodstock sources

Recirculating tanks (107) Earthen ponds(49)

Number of spawn records 136 101

Survival rate per month 0.94 ± 0.02 0.92 ± 0.04

Body weight during spawning (g) 42.80 ± 0.81 39.67 ± 0.65

Number of eggs per spawning (x104) 23.34 ± 0.72 22.45 ± 0.67

Number of nauplii per spawning (x104) 19.85 ± 0.85 19.53 ± 0.83

Hatch rates per spawning (HR) 0.83 ± 0.02 0.85 ± 0.02

HR with arcsine transformation 68.88 ± 1.47 70.40 ± 1.64

Spawn frequencies per female per month 1.34 ± 0.12 1.93 ± 0.23

Fecundity (eggs g-1 of female, x103) 5.51 ± 0.15 5.78 ± 0.19


A significant interaction was also evident for females undergoing multiple

spawning events between treatments (RT vs EP) and female body weight with (BW)

(RT (42.80 ± 0.81 g) vs EP (39.67 ± 0.65) (Table 4.2).


Figure 4.1 a) Pie charts showing the number of spawns for 101 female P. vannamei

broodstock in the recirculating tank treatment (RT) over a one month trial (SF,

number of spawning events); b) Number of spawns for 45 females in the earthen

pond treatment (EP) over a one month trial (SF, number of spawning events).


4.3.2 Effect of Body Size on Individual Reproductive Performance

Hatchery managers in general, currently suggest that body weight of female

broodstock of P. vannamei should be between 30 to 45g (Aquacop, 1983; Robertson

et al., 1993; Wyban and Sweeney, 1991). Mature female body weight at spawning

(BW) was divided into three bodyweight classes in the current study with 38 g and

48 g used as cut-off weights for dividing broodstock females into three size groups

represented by small (<38 g), medium (38-48 g) and large size broodstock female

bodyweight classes (>48 g). A statistically significant interaction was evident for

BW with multiple spawns among the three size classes in both the RT and EP

treatments (Table 4.2). SF for the same body size classes (small, medium, large)

between treatments (RT and EP) however, was not different (Table 4.2).

Large size class females in both treatments (RT and EP) produced significantly

more eggs per spawn than did females in either the small or medium size classes

(P<0.01). No statistically significant differences were evident for NN in either the

small or medium female size classes between treatments. In parallel, no significant

differences were evident for either HR or HRat comparisons, a result indicating that

egg hatchability was not impacted by individual size class of broodstock female

(Table 4.2). Even given a relatively higher mean BW for females in the RT

treatment and a tendency for more frequent spawning (SF), no statistically significant

difference was observed. One interesting result however, was that females in the

medium size class for BW in the EP treatment produced more than twice the number

of total spawning events (SF) compared with small and large size classes in the same

treatment (EP) (P<0.01). While no significant difference (P>0.05) was observed for

the effect of body size on FE for females in the RT treatment, small class females did


show a significantly higher FE (P<0.01) than medium class females in the EP

treatment (Table 4.2).

Table 4.2 Comparison of mean reproductive performance (plus standard errors) of

different size classes of female broodstock reared in earthen ponds (EP) vs

recirculating tanks (RT). Superscript letters indicate significant differences within

and between treatments (rearing conditions) for each reproductive parameter.

Reproductive

parameters

Recirculating tanks Earth ponds

Small n=46 Medium n=53 Large n=37 Small n=51 Medium n=39 Large n=11

BW < 38g BW, 38~48 g BW > 48 g BW < 38g BW, 38~48 g BW > 48 g

BW g 32.43 ± 0.52 a 43.45 ± 0.40 b 54.76 ± 0.83 c 34.47 ± 0.33 a 42.98 ± 0.51 b 52.04 ± 1.25 c

NE (x104) 18.19 ± 0.92 a 24.54 ± 1.06 bc 28.04 ± 1.39 c 22.52 ± 0.91 ab 20.78 ± 1.05 ab 27.99 ± 2.01 c

NN (x104) 15.00 ± 1.09 a 20.92 ± 1.32 ab 24.32 ± 1.74 b 19.93 ± 1.06 ab 17.55 ± 1.35 a 24.74 ± 2.74 b

HR 0.81 ± 0.03 ns 0.84 ± 0.03 ns 0.85 ± 0.04 ns 0.87 ± 0.03 ns 0.81 ± 0.04 ns 0.88 ± 0.07 ns

HRat 67.12 ± 2.47 ns 69.58 ± 2.43 ns 70.07 ± 2.81 ns 72.11 ± 2.16 ns 67.55 ± 2.87 ns 72.57 ± 4.42 ns

SF monthly 1.00 ± 0.20 a 1.42 ± 0.21 a 1.60 ± 0.22 ab 1.29 ± 0.33 a 2.75 ± 0.35 b 1.25 ± 0.37 a

FE (x103)/g 5.61 ± 0.27 ab 5.68 ± 0.25 ab 5.16 ± 0.27 b 6.56 ± 0.27 b 4.88 ± 0.27 a 5.38 ± 0.37 ab

4.3.3 Reproductive Parameters in Relation to Spawning Order

A summary of reproductive parameters in relation to spawning order are

presented in Table 4.3. In general, the pattern shows that individual females that

spawned more eggs in both treatments possessed comparatively greater reproductive

quality compared with females that spawned only once or twice. The trend suggests

that multiple spawners show better reproductive quality in terms of mean number of

nauplii (NN: 22.61×104, RT; 22.72×104, EP) produced, and higher fecundity


estimates (FE: 6.26×103, RT; 6.23×103, EP), even though the pattern was not

significantly different.

Table 4.3 Comparison of mean reproductive parameters (plus standard errors) for

different spawn frequency (spawning once only (1), twice only (2), or three or more

times (3+)) for female broodstock reared in earthen ponds (EP) vs recirculating tanks

(RT). Superscript letters indicate significant differences within and between

treatments (rearing conditions) for each reproductive parameter.

Reproductive

parametersRecirculating tanks Earth ponds

SF 1 n=84 2 n=27 3+ n=25 1 n=54 2 n=22 3+ n=25

NE (104) 22.62 ± 0.90 22.91 ± 1.44 26.21 ± 1.92 21.96 ± 0.87 20.92 ± 1.24 22.96 ± 0.53

NN (104) 18.63 ± 1.07 21.08 ± 1.50 22.61 ± 2.34 18.36 ± 1.13 18.79 ± 1.54 22.72 ± 1.72

HR 0.80 ± 0.03 0.91 ± 0.02 0.85 ± 0.05 0.82 ± 0.03 0.88 ± 0.04 0.90 ± 0.03

HRat 66.31 ± 1.97 75.18 ± 1.93 70.72 ± 3.68 67.86 ± 2.57 72.85 ± 3.05 73.72 ± 2.28

FE (103)/g 5.43 ± 0.17 5.09 ± 0.30 6.26 ± 0.49 5.81 ± 0.25 5.22 ± 0.35 6.23 ± 0.44

4.4 DISCUSSION

For the current study, mean survival rate and means of reproductive parameters of

broodstock were very similar to that reported by commercial hatcheries in China.

Results suggest that our culture conditions were comparable and hence we had

provided near optimal maturation conditions for our experiment.


4.4.1 Comparative Reproductive Performance of Broodstock in the RT and EPTreatments

Overall, we observed very similar results for NE, NN, and HR reproductive

parameters in the two culture test environment treatments (RT vs EP). For spawning

frequency (SF) however, EP stocks spawned at a significantly higher rate than in the

RT treatment. Results for NE, NN and HR traits reported here were also much higher

than results reported in two earlier studies that compared reproductive parameters in

domesticated P. vannamei broodstock in tanks vs EP (Andriantahina et al., 2012b;

Otoshi et al., 2003). These differences may largely reflect use of different breeding

approaches (natural matings – the current study vs artificial insemination – published

studies). Mean NE, NN, and HR estimates reported here are closer to optima

proposed for P. vannamei broodstock performance standards for NE (20×104) and

HR (85%) (Zeigler et al., 2015). Our results are also consistent with another early

study that indicated that broodstock of P. vannamei reared in RT do not show

compromised reproductive parameters compared with wild spawned or pond reared

females (Otoshi et al., 2003). This difference is also reflected in reports of a shorter

inter-spawn period for pond vs tank housed females (Andriantahina et al., 2012).

Differences in SF for females in the RT vs EP treatments are in line with

observations made by some hatchery technicians in China who report that in general,

stocks reared in EP are easier to mature and show higher mating rates per night than

their SPF counterparts (reared in tanks).

Studies of other penaeid species have reported similar findings with culture stocks

trialled in EP, generally showing better reproductive performance compared with

those maintained in tank systems. This effect may be a result of differences in

environmental factors and incorporation of live food for nutrition in EP that may


enhance the productive performance of female shrimp. For P. esculentus, while

broodstock reared in ponds were sufficient for hatchery production in terms of

reproductive performance at a commercial scale, tank-reared females showed

significantly lower spawning rates and lower mean numbers of eggs per spawning

event (NE) (Keys and Crocos, 2006). This suggests that RT environments were

unlikely to be favoured for commercial scale hatchery nauplii production. For P.

monodon, a significant improvement in key reproductive parameters (NE, NN and

HR) was observed in females reared over five months in EP then transferred to RT

systems compared with females reared in RT systems over their complete rearing

period (Coman et al., 2013).

Improving the proportion of multiple spawners in a broodstock population has

been recognized as a key factor for optimizing nauplii production in penaeid species

(Coman and Crocos, 2003; Ibarra et al., 2007b; Racotta et al., 2003). In the current

study over a one month trial, a third of the RT-reared females did not spawn, and a

third only spawned a single time (Figure 4.1a). In contrast, EP-reared females

showed a significantly higher SF than that observed in the RT treatment, with only

20.00% failing to spawn, and almost 40% spawning three times or more (Figure

4.1b). This result is similar to those in a recent report from a commercial P.

vannamei nauplii hatchery in Mexico over a 36 day test period where 48% females

did not spawn, 18% spawned once, 15% spawned twice, while 19% spawned three

times or more (Arcos et al., 2003a). In another study of spawning record for 29 days

on 161 eyestalk ablated females, 44% did not spawn and 14% spawned four times or

more (Arcos et al., 2004). These findings together highlight that multiple spawners

are likely to only represent a relatively small proportion of the total female spawning


population but they do make a very significant contribution to total nauplii

production.

4.4.2 Impacts of Female Body Size on Reproduction Performance

Individual body size is the principle criterion widely used to select broodstock in

penaeid shrimp hatcheries. Results of examining the relationships between

reproductive parameters and individual body size here show clearly that female body

size has a significant impact on reproductive performance for the following traits;

NE, NN, SF, and FE, while there is little or no impact for HR or HRat.

There was a tendency for the large class females in both the EP and RT culture

environments in our study to produce higher NE or NN estimates than smaller

females. This result is also consistent with earlier studies in other penaeid shrimps

where fecundity (NE) has been correlated positively with individual spawner size

(Andriantahina et al., 2012b; Coman et al., 2013; Emmerson, 1980; Hansford and

Marsden, 1995; Marsden et al., 2013; Ottogalli et al., 1988; Palacios et al., 1998;

Peixoto et al., 2008; Wen et al., 2015). It is relatively difficult however, to directly

compare our results for HR with other studies because reported HR ranges vary

widely, particularly in early studies. It is worth noting however, that the HR

estimates reported here were all within the recognised current optimal range for

commercial nauplii production of P. vannamei stocks in China. HR is known to be

closely linked to the relative physiological condition of individual female broodstock

and management of the maturation environment in test tanks.

It is interesting to note that while larger females in general tended to show a

higher SF rate, females in the medium size class group in the EP treatment had a SF

mean of more than double that of small and large class females in the same treatment,


respectively. This phenomenon potentially could be explained by different strategies

for allocating energy among size classes. We hypothesise that, after maturation,

females in the EP medium size class likely directed more energy towards

reproduction rather than to allocating resources for further growth (i.e. growth rate

slowed and individuals spawned multiple times while those individuals in the larger

size class continued growing and spawned less frequently). It is likely that these

observed differences for SF in interactions between body size group and treatment

are also reflected in the different selection criteria used for P. vannamei broodstock

currently in the shrimp farming industry in China where large sized female SPF

individuals (raised in tanks) are considered better stock while small entrepreneur

hatcheries using their own culture lines prefer small and medium size class females

as broodstock. Our FE results add weight to this observation for a preference for

small and medium size class females in smaller entrepreneurial hatcheries, because

high FE of small-medium size results in greater egg production. This is because no

relationship was evident between body size and FE for females raised in an RT

environment whereas FE of small size females in the EP treatment was higher than

the other two size groups in this treatment. SF has also been reported to be positively

correlated with large female size and this size class for females also showed a higher

spawning frequency (Andriantahina et al., 2012b; Arcos et al., 2003a; Menasveta et

al., 1994; Palacios et al., 2000).

The minimum size of adult SPF females currently supplied to farmers in China

ranges from 35g to 45g. Threshold body size (38g) between small and medium size

classes in our study, in general accords well with the recommended size for P.

vannamei female stocks as breeders. In general, 30-45g individuals can be used for

nauplii production in a hatchery (Aquacop, 1983), even though some animal breeders


have advised use of even larger females of up to of 45g because they may perform

better (Robertson et al., 1993; Wyban and Sweeney, 1991).

4.4.3 Quality of Reproductive Performance in Relation to Spawning Order

It was clear from our results that no compromise was evident for NE, NN, HR, or

FE reproductive parameters in multiple spawners, or even that multiple spawners

were better in terms of mean NN or FE. Our results also support some earlier studies

that show offspring quality was not negatively impacted by spawning order for a

variety of key reproductive parameters including fecundity, fertilization rate,

hatchery, or biochemical variables that in general, reflect reproductive quality (Arcos

et al., 2004; Arcos et al., 2003a; Palacios and Racotta, 2003; Peixoto et al., 2004).

In contrast, a series of earlier studies reported that a deterioration in the

reproductive capacity of broodstock females can result from reproductive exhaustion

and that this is correlated with spawning order in penaeid species (Emmerson, 1980;

Hansford and Marsden, 1995; Marsden et al., 1997; Mendoza et al., 1997; Palacios et

al., 1999a; Palacios et al., 1999b). Differences for results between studies however,

may result from time factors. It is quite common for female penaeids to show a

decline in reproductive capacity under captive maturation conditions after unilateral

eyestalk ablation (Bray et al., 1990; Menasveta et al., 1993; Palacios et al., 1998;

Palacios et al., 1999b; Wyban, 1997). In general, experiments that test spawning

quality in relation to spawning order are undertaken over a relatively long time frame

(30-40 days). Reproductive data on multiple spawns as a result, are often collected

later over the experimental time period experiment than is data for ‘first order’ or

‘second order’ spawns. As a consequence, the time factor for measuring ‘exhaustion’

effects are very different and could significantly impact results between earlier


studies and more recent ones that have used natural spawning. In particular, if

maturation tank conditions were sub-optimal or diet had been insufficient to supply

adequate nutritional requirements.

In our study, data were collected during the second month after a female had

experienced unilateral eyestalk ablation, so production of nauplii occurred over a

stable period. Furthermore, mortality rates of broodstock and estimates of

reproductive parameters in the current study indicate that near optimal maturation

conditions were supplied to the broodstock tested. As a consequence, this would

likely minimise any impacts of test time on potential for reproductive exhaustion.

Again, this highlights the difficulties with dealing with domesticated penaeid

broodstock studies and how to establish the best, uniform standard experimental

conditions that will allow meaningful comparisons to be made between different

studies.


4.5 CONCLUSIONS

In conclusion, results here indicate that no significant differences were evident for

the majority of reproductive performance traits tested between female Pacific white

shrimp broodstock reared in RT vs EP environments. Females in the EP treatment

however, produced more nauplii per individual than females raised in an RT

environment and this resulted from a significantly higher SF rate while no evidence

was observed for reproductive exhaustion related to the number of consecutive

spawns. Nauplii production in hatcheries therefore, potentially can be optimized by

employing different strategies in relation to female broodstock body size selection.

When RT-reared stocks are used, selecting larger body size females should result in

higher nauplii production levels, while for small-scale farmers who use EP-reared

stocks, use of female broodstock in the medium size class range should maximize

nauplii production. In the next chapter, the additive genetic components for

reproductive traits examined here in our culture line will be assessed to decide if

these traits can be improved via genetic selection to maximise reproductive output in

cultured P. vannamei stocks in China.

137 Chapter 5: Quantitative Genetic Assessment of Female Reproductive Traits in a DomesticatedPacific White Shrimp (Penaeus vannamei) Line in China

Chapter 5: Quantitative Genetic Assessment of Female

Reproductive Traits in a Domesticated Pacific White Shrimp (Penaeus

vannamei) Line in China


ABSTRACT

Earlier (Chapter 4), we reported that optimising P. vannamei broodstock

reproductive performance in combination with selection for large body size could

improve productivity of females in two different culture environments. In parallel,

seed production can be improved if genetic selection is applied to key reproductive

traits when substantial additive genetic variation is present that could be exploited in

a breeding program. Despite the commercial importance of reproductive traits to the

seed production sector, to date few quantitative genetic studies have been conducted

on these traits in farmed penaeid shrimp culture lines. Here, we investigated genetic

parameters for some important reproductive traits that directly impact nauplii

production in Pacific white shrimp hatcheries in China. Our objectives were to

improve broodstock reproductive quality, and to anticipate any potential impacts on

reproductive performance when selecting for increased body weight by assessing

genetic correlations between post-spawning body weight and specific female

reproductive traits. Data were collected on 595 females from 78 fullsib families over

30 days, with a total of 1,113 spawning events recorded. Traits studied included:

body weight after spawning (WAS), number of eggs per spawn (NE), number of

nauplii per spawn (NN), egg hatching rate per spawn (HR), number of eggs produced

relative to female weight (g) (FE), and spawn frequency over 30 days (SF).

Heritability estimated high for WAS (h2 = 0.64 ± 0.10) and moderate for NE (0.26 ±

0.07), NN (0.18 ± 0.06), and SF (0.15 ± 0.06), respectively. On contrast, h2 for HR

(0.04 ± 0.03) and FE (0.05 ± 0.04) were low. The genetic correlations between

growth trait (WAS) with NE, NN and SF were 0.93 ± 0.10, 0.84 ± 0.10, and 0.57 ±

0.18, respectively. While the genetic correlation between WAS and HR was low

(0.02 ± 0.33), a negative genetic correlation was found between WAS and FE (-0.50


± 0.27). Overall, we concluded that it is possible to improve key female reproductive

traits (i.e. NE, NN, and SF) in cultured white shrimp lines via genetic selection, but it

was unlikely for HR or FE. The genetic relationship between growth rate and key

female reproductive traits indicates that selection for fast growth can in parallel

enhance production in the seed sector, with little or no compromise on egg quality.

Keywords: Penaeus vannamei, Heritability, Genetic parameters, Reproductive traits


5.1 INTRODUCTION

Reproductive characteristics constitute a set of commercially important traits that

are yet to receive much attention when genetic improvement is applied to farmed

aquatic species (Gjedrem, 2012; Gjedrem and Rye, 2018; Nguyen, 2016). This is

especially true for penaeid shrimp species that possess many unique reproductive

characteristics; in particular, at maturation in hatcheries, many females may spawn

relatively infrequently or may never spawn, while a small proportion of females

spawn multiple times, hence these females are likely to contribute the majority of

nauplii produced (Arcos et al., 2003a; Benzie, 1997; Ibarra et al., 2007b).

Developing knowledge about genetic parameters (heritability and genetic

correlations) for key reproductive traits will be essential for designing better breeding

strategies and for improving broodstock reproductive capacity via genetic selection.

For example, a recent experimental study of a small marine copepod crustacean

(Parvocalanus crassirostris) reported that total egg production was increased by

24.5% following selection over five generations with heritability (h2) for this trait

estimated at 0.38 (Alajmi et al., 2014).

While application of genetic selection methodologies have increased productivity

significantly in a number of farmed aquatic species (De Verdal et al., 2018; Gjedrem

et al., 2012), most breed improvement programs have focused primarily on growth

traits (Gjedrem and Rye, 2018; Janssen et al., 2017; Ren et al., 2018; Sae‐Lim et al.,

2016). There are many examples of selection for improving growth traits however,

that have also reported undesirable correlated effects on other fitness traits in

domesticated animals, particularly with respect to metabolic, reproductive and health

status traits (Rauw et al., 1998). Significant evidence for correlated negative effects


have been reported in poultry and livestock animals. As an example, negative

correlations were reported in pigs for a number of reproductive traits when

individuals were selected for high meat production efficiency. This resulted in a

significant reduction in both fertility and litter size (Rauw et al., 1998). In contrast,

studies in a wide variety of animal species (including model species like mice,

rabbits, dogs, sheep, pigs and fish) have reported positive relationships between

growth traits and some reproductive traits (e.g. litter size and fecundity) (Bünger et

al., 2005). While the practice of genetic selection works essentially via a ‘black box’

approach, the opportunity still remains to understand, to anticipate and to prevent any

potentially negative impacts of selection based on developing knowledge about

genetic correlations (rg) between growth traits and important reproductive traits

(Rauw et al., 1998).

To date, few studies have investigated genetic parameters for reproductive traits in

farmed aquatic taxa, and of those studies that have been conducted, most have

focussed on farmed salmonid species (Gall and Huang, 1988; Gall and Neira, 2004;

Hao and Chen, 2008; L'Abée‐Lund and Hindar, 1990; Neira et al., 2006; Su et al.,

1997) and tilapias (Thoa et al., 2017; Trọng et al., 2013a; b). In salmonid species, the

majority of productive traits that have been investigated show relatively high levels

of additive genetic variance with most genetic correlations indicating positive

relationships between growth-related traits and the reproductive traits examined. As

an example, Gall and Neira (2004) reported h2 estimates for weight, number of green

eggs, and number of eyed eggs in coho salmon (Oncorhynchus kisutch) ranging

between 0.32 to 0.42. In domesticated rainbow trout (Oncorhynchus mykiss), h2

estimates for female egg number and egg size were 0.32 and 0.28, respectively (Gall


and Huang, 1988), while h2 estimates for traits including spawning date, egg size,

number of eggs, and egg volume were all moderate to high (Su et al., 1997). In

contrast in a Nile tilapia (Oreochromis niloticus) breeding program in Vietnam, h2

estimates for both fecundity traits and fertility were low (Trọng et al., 2013a), while

spawning success (h2 = 0.20-0.22) was an exception (Trọng et al., 2013b). Likewise,

Thoa et al. (2017) reported low h2 estimates for number of fry at hatching, total fry

weight, and fry mortality in a base population of red tilapia (Oreochromis spp.)

(Thoa et al., 2017). In addition, a case study that applied selection on reproductive

traits in female channel catfish to produce hybrid catfish embryos reported realized

h2 estimates for fecundity ranging from 0.10 to 0.42 while h2 for percentage hatch

and fry/kg were low (Gima et al., 2014).

While a number of studies have demonstrated the potential capacity to improve

seed production via genetic selection (Arcos et al., 2003a; Benzie, 1997; Ibarra et al.,

2007b), there have been few studies published on quantitative genetic analyses of

reproductive traits in penaeid shrimps. In particular, there has been a noticeable lack

of studies that have investigated capacity of females to spawn multiple times. This

trait has the potential to double or even triple nauplii production by individual

females. Until recently however, there has only been a single report that has

investigated genetic parameters for this trait in penaeids (Ibarra et al., 2005).

Reproductive traits in farmed penaeid species (specifically fecundity-related traits),

are among those that can contribute the most to increasing profitability of the

hatchery sector but to date, only a single study has reported h2 estimates for the above

traits under commercial hatchery conditions (Arcos et al., 2004). While there have

been two studies estimating genetic parameters for reproductive traits in Pacific

white shrimp (P. vannamei) females following artificial insemination (Caballero‐


Zamora et al., 2015; Tan et al., 2017b), more information will be required for the

seed production sector because these studies indicate that there are differences in

reproductive trait data when applying artificial insemination vs natural mating in

penaeid species. Moreover, high levels of additive genetic variance in reproductive

traits for mean oocyte number, diameter, ovary maturity stage (Arcos et al., 2005b),

and high genetic correlations between levels of vitellogenin in haemolymph and

mean diameter of oocytes have been reported for Pacific white shrimp (Ibarra et al.,

2009). In black tiger shrimp (P. monodon), h2 estimates for days to spawn, number of

eggs, number of nauplii, and hatching rate ranged from 0.18 to 0.47 indicating that

these traits can be improved to increase individual reproductive output via genetic

selection (Macbeth et al., 2007).

The aims of the current study therefore were: (1) to examine genetic variance for

key reproductive traits in female P. vannamei broodstock under commercial

maturation conditions using the base line developed in a complete 4×4 diallel cross,

and (2) to estimate genetic correlations between individual body weight after

spawning with specific reproductive traits. Knowledge generated in this study can be

applied to improve production of nauplii via genetic selection and to minimize any

negative impacts on reproductive performance that may result from selection for fast

growth.



5.2.1 Experimental Families

The experiment was conducted at a Beijing Shuishiji Biotech. Ltd. maturation

facility in Wanning (Hainan Province), China. A base family line of P. vannamei

was produced following a complete 4×4 diallel cross of four domesticated culture

lines obtained in China between June and July 2015. Details of the strains and

maintenance of families generated are described in Ren et al. (2018) and in Chapter 3.

When shrimp had reached maturation stage (age of 10 month) 660 test animals were

randomly selected from each family (in total 78 families, for details of family

production see Chapter 3 Methods Section) and individuals transferred to maturation

tanks (10 m2). Females were then subjected to unilateral eyestalk ablation to induce

ovary maturation, and broodstock males and females reared separately with 110

individuals per tank.

Number-coded silicon eye rings were tagged on the remaining eyestalk for

individual female identification. In total, data from 595 females representing 78 full-

sib families were recorded in the study. A biological water recirculating system was

used to maintain water quality at an exchange rate of 600% to 800% per day. Daily

feed composition consisted of a mixture of fresh meal diet (50% polychaetes, 30%

squid and 20% mussels) delivered at a rate of approximately 5% of total biomass per

tank. Tank water was maintained at 28 ± 2 °C and 31-35 ppt salinity. Data collection

commenced on Jun 3rd and continued over 30 days.

5.2.2 Measurement of Reproductive Traits

Females with mature ovaries (stage IV) were collected daily at 10:00 and

transferred to tanks containing male broodstock. At 19:00, successfully mated

females with attached spermatophores were placed in individual 500 L fibreglass


tanks filled with 300 L of clean seawater. Environmental conditions for spawning

were maintained at 28 ± 0.5 °C at a salinity of 32-36 ppt. At 24:00, all females in

spawning tanks were returned to maturation tanks and eggs remaining in the

spawning tanks were incubated with gentle aeration.

Body weight after successful spawning (WAS) was measured at 12:00. Five

reproductive traits were measured and recorded: i) number of eggs (NE) per spawn

measured in three replicate 200 ml breaker samples after being thoroughly

homogenized in seawater taken from the spawning tanks (300 L in volume); ii)

number of nauplii (NN) per spawning measured in the same way as for NE after

nauplii had hatched on the second day at 11:30; iii) relative fecundity, measured as

the number of eggs per unit body weight (FE), calculated by dividing the number of

eggs per spawn by WAS; iv) egg hatching rate per spawn (HR); and v) female

spawning frequency (SF), calculated as the total number of successful spawning

events per individual female at the end of the experimental period.


Prior to formal data analysis, raw HR percentage data were arcsine transformed

(Zar, 1996). For comparative purposes, both the original data (HR) and arcsine

transformed data (HRat) were used in subsequent statistical analyses. For all other

traits (WAS, NE, NN, FE and SF), untransformed values were used in the data

analysis. Genetic variance and covariance components and h2 for targeted traits were

estimated using an animal model applying the restricted maximum likelihood

(REML) methodology in WOMBAT (Meyer, 2007). For SF, the linear mixed animal

model can be written as follows:

y = Xβ + Zα + e, (1)


Where:

y is a vector of observations for a reproductive trait per spawn event (WAS,

NE, NN, HR, HRat, FE, or SF),

β is a vector of fixed effects consisting of maturation tanks, the recorded

spawn event batches, and regression coefficient of age of shrimps,

α is the vector of random additive genetic effects of the animals,

e is the vector of random residual errors and

X and Z are known incidence matrices relating observations to the fixed effects

mentioned above, and animal effects, respectively. Both a and e follow a normal

distribution with mean zero and variance Aσa2 and Iσe2, respectively. Here, σa2 and

σe2 are the additive genetic and residual error variances while A is the numerator

relationship matrix based on pedigree information.

In this experiment, estimates of WAS, NE, NN, HR, HRat and FE resulted from

multiple observations for some female individuals. Repeated measures for these traits

were across 30 days, with these data recorded from different spawning order, i.e. first

spawn, second spawn, up to sixth spawn for some females. Therefore, we also used a

repeatability animal model (Model 2) to account for replication within individuals:

y = Xβ + Z1α + Z2pe + e, (2)

Where:

y is a vector of observations for a reproductive trait per spawn event (WAS,

NE, NN, HR, HRat, or FE),

β is a vector of fixed effects consisting of maturation tanks, the recorded


spawn event batches, and regression coefficient of age of shrimps,

α is the vector of random additive genetic effects of the animals,

pe is the vector of random maternal permanent environment effects

contributed by individual females to their offspring families,

e is the vector of random residual errors and

X, Z1 and Z2 are known incidence matrices relating observations to the fixed effects

mentioned above, animal effects and permanent environment effects, respectively. a,

pe and e follow a normal distribution with mean zero and variance Aσa2 , Iσpe2 and

Iσe2, respectively. Here, σa2, σpe2 and σe2 are the additive genetic, permanent

environment and error variances while A is the numerator relationship matrix based

on pedigree information.

In Model (1), h2 was calculated as the ratio of additive genetic variance to total

phenotype variance (h2= σα2/ σp2). In Model (2), heritability (h2) and repeatability

(rep) were calculated as follows:

�2 �σ�2

σ�2 � σ�鍘2 � σ鍘2� �鍘� �

σ�2 � σ�鍘2

σ�2 � σ�鍘2 � σ鍘2

In the primary analysis, the results were similar between the linear mixed animal

model and the repeatability animal model, which were also supported by comparing

the log-likelihood ratio of the models. Therefore, a multivariate mixed animal model

was fitted to estimate the genetic and phenotype correlation between examined traits,

expressed in matrix notation as:


��th�tt�th�th��t

� �� (3)

Where yWAS, yNE, yNN, yHR, yFE and ySF are the same as defined in Model

1, respectively. Total phenotypic variance (σp2) was estimated as the sum of additive

animal genetic variance (σα2) and random residual components (σe2). Genetic or

phenotypic correlation between two traits was calculated as: � � �12

�12 �2

2where σ12

was the genetic or phenotypic covariance between two traits, and σ12 and σ22 were

either additive genetic variances of trait 1 and 2, or phenotypic variances of the two

traits, respectively.

5.3 RESULTS

5.3.1 Descriptive Statistics

Over the 30 day experiment, 950 successful spawning records were observed out

of a total of 1113 spawning events. The number, mean values, minimum and

maximum values, standard deviations and coefficients of variation for each trait are

presented in Table 5.1. Mean body weight after spawning (WAS) was 39.66 ± 8.44 g

(ranging from 18.19 to 70.63 g). Mean number of eggs (NE, 225.15 × 103) and

number of nauplii (NN, 194.63 × 103) per spawning were in general terms,

comparable with that obtained during commercial Pacific white shrimp nauplii

production in China (pers. obs.). The high mean hatch rate (HR = 84.60%) indicates


that the experimental broodstock management protocols employed had been

appropriate.

Table 5.1 Descriptive statistics of reproductive traits for female Penaeus vannamei.

WAS = Weight after spawning, NE = Number of eggs per spawning, NN = Numberof nauplii per spawning, HR = egg hatching rate %, HRat= Arcsine transformed HR,FE = Number of eggs per unit weight, SF = Number of spawns during the 30 dayexperiment period.

Traits Unit N Mean Minimum Maximum Standarddeviation

Coefficientvariation (%)

WAS g 947 39.66 18.19 70.63 8.44 21.27NE ×103 949 225.1

533.00 677.25 79.97 35.52

NN ×103 949 194.63

0.00 616.50 91.94 47.24HR % 950 84.60 0.00 1.00 0.23 27.57HRat unit 950 69.83 0.00 90.00 17.99 25.76FE ×103/g 946 5.74 1.35 12.80 1.82 31.76SF unit 595 1.44 0.00 6.00 1.34 93.40


5.3.2 Relationships Between Body Weight and Number of Eggs/Nauplii per Spawn

The phenotypic correlation between body weight after spawning (WAS) and

number of eggs per spawn (NE) was moderate (r = 0.45) yet highly significantly

different from zero (P<0.01). Results of a linear regression analysis between WAS

and NE are presented in Figure 5.1. Similarly, number of nauplii per spawning (NN)

was also positively correlated with post-spawning weight (r = 0.35, P<0.01 - Figure

5.2).


eggs per spawn (NE), NE (103) = 4.341 × WAS (g) + 54.427.



nauplii per spawn (NN), NN (103) = 3.964 × WAS (g) + 42.144.

5.3.3 Frequency Distribution of Number of Females Spawning

Figure 5.3 presents data on the distribution of spawning records for 595 females.

Greater than half of the female population did not spawn or spawned only a single

time over the trial period, while 21.85% of females recorded two spawning events.

Essentially, only a very small percentage of females who were multiple spawners (SF

= 3+) in the sample contributed an unequally large proportion (43.2%) of all progeny

spawned.


Figure 5.3 Number of spawns for 595 females over the 30 day trial (SF, spawning

frequency).

5.3.4 Genetic (Co)variances Among Traits

Table 5.2 presents the estimated variances and h2 values for each reproductive

trait. h2 for body weight after spawning (WAS) was very high (0.64 ± 0.10), while h2

for the number of eggs per spawn (NE), number of nauplii per spawn (NN) and

spawn frequency (SF) were all moderate (Table 5.2, ranging from 0.15 to 0.26). h2

estimated for hatching rate of eggs (HR and HRat) and the number of eggs per unit

body weight (FE) were in general, low and not significantly different from zero. This


result indicates that only limited additive genetic variance was present in the line for

HR and FE traits.

Table 5.2. Estimates of variance components, heritability values (h2) and

repeatability (rep) for WAS, NE, NN, HR, HRat, FE and SF. σ2p, phenotypic variance;

σ2a, additive genetic variance; σ2Ep, permanent environment variance.

*Estimate is highly significantly different from zero (P<0.01).

NS Estimate is not significantly different from zero (P>0.05).

WAS, NE, NN, HR, HRat, FE and SF: see legend in Table 1.

NA, Not applicable.

5.3.5 Genetic and Phenotypic Correlations among Reproductive Traits

Table 5.3 presents both genetic (rg) and phenotypic (rp) correlations between pairs

of reproductive traits. The genetic correlations between WAS and reproductive traits

(NE, NN, and SF) were high to medium, being 0.93 ± 0.10, 0.84 ± 0.10, and 0.57 ±

TraitsVariance components Heritability (±SE) Repeatability (±SE)

σ2p σ2a σ2Ep h2 rep

WAS 60.13 38.47 3.15 0.64 ± 0.10 * 0.69 ± 0.13 *

NE 5735.74 1467.83 23.81 0.26 ± 0.07 * 0.26 ± 0.07 *

NN 7853.15 1384.53 101.17 0.18 ± 0.06 * 0.19 ± 0.06 *

HR 0.05 0.00 0.00 0.04 ± 0.03 NS 0.04 ± 0.03 NS

HRat 311.68 12.75 1.32 0.04 ± 0.03 NS 0.04 ± 0.03 NS

FE 2.85 0.14 0.00 0.05 ± 0.04 NS 0.05 ± 0.04 NS

SF 1.79 0.26 NA 0.15 ± 0.06 * NA


0.18, respectively. The genetic correlation between WAS and HR was low (0.02 ±

0.33) with associated large standard errors. A negative genetic correlation (-0.50 ±

0.27) was observed however, between WAS and NE suggesting that while NE and

NN will be increased with body weight, FE the ratio of number of eggs per biomass

will be decreased. Regarding NE, NN, and SF, these traits directly determine the

production for nauplii hatcheries, and HR is an important index of egg quality. The

above genetic relationships suggest that selection for fast growth will also benefit the

seed sector, with no negative impact on egg quality. A negative genetic correlation

between WAS and FE however, indicates that selection for fast growth may involve

a trade-off in relative fecundity. Across all pairwise comparisons among reproductive

traits, the highest genetic correlation were 0.97 ± 0.03 (NE and NN), 0.86 ± 0.18 (SF

and NE), and 0.70 ± 0.24 (SF and NN), respectively.

A similar pattern was evident for phenotypic correlations (rp) as was seen for

genetic correlations among the traits evaluated. All phenotypic correlations were

positive, with the highest estimate evident between NE and NN and the lowest

estimate observed between SF and FE. Importantly, rp estimates among traits in the

current study were good predictors of rg estimates, a result that supports Cheverud’s

(1988) observation that phenotypic correlations are often assumed to reflect

genotypic correlations.


Table 5.3 Estimated genetic (below diagonal) and phenotypic correlations (above

diagonal) for body weight at spawning and reproductive traits* (estimates ± se).

*For abbreviations see legend in Table 5.1.

WAS NE NN HR FE SF

WAS 0.45 ± 0.03 0.35 ± 0.03 0.02 ± 0.04 -0.12 ± 0.09 0.23 ± 0.02

NE 0.93 ± 0.06 0.87 ± 0.01 0.23 ± 0.04 0.80 ± 0.02 0.10 ± 0.04

NN 0.84 ± 0.10 0.97 ± 0.03 0.65 ± 0.02 0.72 ± 0.02 0.14 ± 0.01

HR 0.02 ± 0.33 0.31 ± 0.37 0.55 ± 0.29 0.22 ± 0.04 0.12 ± 0.01

FE -0.50 ± 0.27 -0.17 ± 0.42 -0.02 ± 0.42 0.40 ± 0.55 0.03 ± 0.03

SF 0.57 ± 0.18 0.86 ± 0.18 0.70 ± 0.24 -0.18 ± 0.45 0.44 ± 0.49


5.4 DISCUSSION

5.4.1 The Experiments

The major difficulty associated with studying reproductive traits in penaeid

species is establishing optimal maturation environmental conditions over the

experimental test period because the majority of reproductive traits tested are highly

sensitive to even small variation in physical conditions in maturation tanks,

nutritional factors in broodstock diet and the physiological condition of experimental

animals (Benzie, 1997; Racotta et al., 2003). Across the 30 day experimental period

in our trial, the majority of data on reproductive traits (Table 5.1) were very similar

to those observed in the best performing Pacific white shrimp nauplii commercial

hatcheries in China indicating in general, that our system for broodstock maturation

and management was appropriate. While accumulated spawning frequency (6.2%)

per night in our study (percentage of females in the population spawning per night)

was lower than in some high performing commercial nauplii hatcheries (optimal

mating rate range is 12% - 15% (Zeigler et al., 2015)), it was still within the range

routinely observed under commercial conditions (5% - 12% per night (Briggs, 2006)).

Our relatively low average accumulated matings per night may have resulted from a

size effect of female broodstock used in the experiment as no body size selection of

experimental animals was practiced here from PL to maturation stage. The relatively

large proportion of small sized female broodstock used here potentially may have

resulted in a low spawning frequency compared with if larger individuals had been

chosen for the study specifically. While we documented this effect earlier (see

Chapter 4), positive correlations between body size of broodstock and spawning

frequency have been reported in a variety of farmed penaeid species (Coman et al.,

2013; Marsden et al., 2013; Palacios et al., 1999a; Peixoto et al., 2008; Wen et al.,

2015).


The mean number of eggs per spawn (NE) reported here (225.15×103) was

consistent with estimates reported in two other studies on reproductive traits in

female P. vannamei in Mexico (mean= 217.90 × 103 (Arcos et al., 2004); 216.0 × 103

(Caballero‐Zamora et al., 2015)), but higher than values (160.7 ×103) reported by

Tan et al., (2017b). Estimates of nauplii per spawn (NN) and hatching rate of eggs

(HR) in our study were similar to estimates reported by Arcos et al. (2004)

(NN=187.8×103, HR=86.20%), but significantly higher than those reported by

Caballero-Zamora et al. (2015) (NN=47.0×103, HR=21.76%) and Tan et al. (2017)

(NN=34.7×103, HR=21.59%), respectively. The different results likely arise from

differences in the mating design methods used for data collection between studies as

the latter two studies employed artificial mating while our study and that of Arcos et

al. (2004) employed natural matings. Many factors, such as physical conditions of

male/female broodstock or the artificial insemination (AI) skills of technicians, can

significantly impact performance, which can lead to low hatching rates and a low

number of nauplii produced. Because our experiment relied on natural matings, the

conditions were very similar to commercial production conditions for nauplii

production in China. We would expect therefore, that the data produced would be

more applicable to improving seed reproduction in industrial environments.

The distribution pattern for spawn frequency per female for 595 females (Figure

5.3) was similar to that observed for nauplii production in most penaeid species

where only a small relative proportion of the mature female population are multiple

spawners and they contribute proportionally, the majority of nauplii produced in a

spawning population (Bray et al., 1990; Ibarra et al., 2005; Palacios et al., 1999a)

with approximately a third of all females failed to spawn at all. Mean spawning


frequency (SF) across the 30 day trial in our study was 1.44, lower than reported in

the study by Arcos et al. (2004) where mean SF was 1.71 per female per month. This

difference may result from the different culture conditions used for broodstock in the

two studies. In the Arcos et al. (2004) study, broodstock were cultured in an earthen

pond while broodstock used in our study were cultured from PL to maturation stage

in recirculating tanks. It is quite common for broodstock cultured in earthen ponds to

show higher mean spawning frequency than broodstock cultured in tank systems.

This effect was observed in the previous chapter where we compared data on

reproductive traits between broodstock reared in earthen ponds and recirculating

tanks and observed this effect. In this comparison, mean SF for broodstock raised in

earthen ponds was 1.93, a result similar to that reported by Arcos et al. (2004), but

significantly higher than that of broodstock raised in recirculating tanks (SF = 1.34).

5.4.2 Heritability Estimates

h2 for body weight after spawning (WAS) was high (0.64 ± 0.10), and while

higher than many reports for this trait in other penaeid species it was still within the

normal range observed. For Pacific white shrimp, mean h2 estimates for body weight

during insemination were high at 0.44 ± 0.08 (Caballero‐Zamora et al., 2015) and

0.49 ± 0.14 (Tan et al., 2017b), respectively, while in black tiger shrimp, h2 for body

weight at 54 weeks was 0.53 ± 0.14 (Macbeth et al., 2007). For the same trait in Nile

tilapia, h2was 0.68 ± 0.10 (Trọng et al., 2013a). h2 for body weight prior to spawning

in red tilapia was higher and reported to be 0.80 ± 0.16 (Thoa et al., 2017). While

levels of quantitative genetic variation for specific traits do vary among farmed

populations and species (e.g. crustaceans vs fish), in general there are some

consistencies (Hill, 2010). In coho salmon, h2 estimates for both body weight at

spawning and post-spawn weight were only moderate (Gall and Neira, 2004). So


comparatively, based on our estimates of Pacific white shrimp and the most recent

published studies, body weight at spawning stage in most aquaculture species

appears to be a highly heritable trait, indicating that a large amount of additive

genetic variance is available that can be exploited in breed improvement programs in

most aquatic species that have been tested in this way. Taken together, in general

high heritability for weight/size at maturation stage provides good support for Hill’s

(2010: p79) argument that ‘Heritabilities (h2) tend to be highest for conformational

traits and mature size, typically 50 per cent or more, and lowest for fitness-associated

traits such as fertility (Falconer and Mackay, 1996; Lynch and Walsh, 1998;

Mousseau and Roff, 1987)’.

The h2 estimate for SF was moderate (h2 = 0.15) and is similar to estimates in

earlier reports for this trait in P. vannamei (Ibarra et al., 2005). Moderate levels of

additive genetic variation for SF indicate that multiple spawning capacity is an

inherited trait and can therefore in theory, be improved via selective breeding. h2

estimates for NE (h2 = 0.26) and NN (h2 = 0.18) here were similar with estimates

reported for black tiger shrimp (Macbeth et al., 2007), but higher than estimates

reported in other Pacific white shrimp studies (Arcos et al., 2004; Caballero‐Zamora

et al., 2015; Tan et al., 2017b). Notably, h2 estimates for NE in the study by Arcos et

al. (2004) reported large standard errors probably resulting from the limited number

of families trialled in their study. Variation between our estimates and those of

Caballero-Zamora et al. (2015) and Tan et al. (2017) on the same species, once again,

likely result from the different mating designs employed, in particular use of natural

mating vs an artificial insemination approach. In salmonid species, h2 estimates for

number of eggs and eyed eggs are usually moderate to high (Gall and Huang, 1988;


Gall and Neira, 2004; Su et al., 1997). While in contrast, in tilapia, estimates for

fecundity related traits are often quite low (Thoa et al., 2017; Trọng et al., 2013a).

This however, is not always the case for farmed tilapia strains because number of

eggs (h2 = 0.20) and number of hatched fry (h2 = 0.16) showed moderate heritability

estimates in the GIFT tilapia strain (Hamzah et al., 2016).

Our h2 estimate for relative fecundity FE (h2 = 0.05) is the first report for this trait

in a penaeid species. A low FE estimate for Pacific white shrimp was consistent with

estimates for this trait in Nile tilapia (Trọng et al., 2013a). Likewise, nearly zero

additive genetic variance was reported for a similar trait (fry/kg of broodstock) in

channel catfish (Gima et al., 2014). In general, low and non-significant additive

genetic variance for FE traits suggest that relative reproductive output would most

likely be a difficult trait to improve via genetic selection in most aquatic species.

h2 estimates for egg hatching rate (HR) estimated in our study were also low, a

result consistent with similar estimates in both tilapia (Thoa et al., 2017; Trọng et al.,

2013a) and channel catfish (Gima et al., 2014). A related trait (number of larvae per

female at hatching) reported for giant freshwater prawn also showed low additive

genetic variance (Vu and Nguyen, 2018). In general, traits that involve fertilization

and/or survival of larvae at hatching are essentially group fitness traits rather than

individual ones, indicating that potentially many non-genetic factors can have

significant effects on such traits. Overall, limited additive genetic variation for HR

indicates that this trait in Pacific white shrimp is unlikely to be improved via genetic

selection.

5.4.3 Genetic and Phenotypic Correlations

Estimated genetic correlations between body weight of WAS and fecundity traits

(NE, NN, FE, and SF) were all positive and moderate in level (from 0.57 to 0.93), a


result that agrees with earlier reported results for P. vannamei, of rg 0.54 between

body weight and NE, and rg 0.49 between body weight and NN, respectively

(Caballero‐Zamora et al., 2015). Positive genetic correlations that are, in general

moderate in degree between bodyweight at spawning and fecundity traits have also

been reported in tilapia (Trọng et al., 2013a) and some salmonid species (Gall and

Huang, 1988; Gall and Neira, 2004; Su et al., 1997). The genetic correlation between

WAS and HR in our study was low and not significantly different from zero, which

suggests that the genetic control of these two traits are not linked. Genetic

correlations between WAS and other reproductive traits tested here were all positive,

a result that suggests that selection to increase body weight of female broodstock will

not have any negative impacts on their individual reproductive performance, and in

fact could actually increase overall female broodstock reproductive output via

indirect selection. In aquatic species, broodstock with relatively high body weight at

spawning usually have experienced good nutrition and are likely to be in a robust

physiological condition, as a consequence their fecundity should be higher than

individuals that have experienced poorer nutrition. The genetic correlations results

here for P. vannamei females however suggest, that high body weight and better

reproductive performance are linked.

Genetic correlations among reproductive traits (NE, NN, HR, FE and SF) were all

positive except for that of SF vs HR, and ranged from low (NE vs HR) to high (NE

vs NN). These results show clearly that genetic selection on individual reproductive

traits is unlikely to have any potentially negative impact on other reproductive traits.

While this set of reproductive traits is very important for nauplii production in

commercial Pacific white shrimp hatcheries, to date no published studies have


reported on genetic correlations among these traits, so it is not possible to make a

comparison of our results with those of others. Data from other studies however, that

have employed artificial insemination rather than natural matings in penaeids have

reported on correlations between some of these traits. As examples, the genetic

correlation between NN and NE was reported to be moderate (0.24 ± 0.41) in P.

vannamei (Caballero‐Zamora et al., 2015). For black tiger shrimp Macbeth et al.

(2007), reported negative genetic correlations between NE and NN, and NE and HR,

but moderate and positive correlations between NN and HR. In other aquaculture

species, results of estimates for genetic correlations among reproductive traits have

been very diverse (Gall and Neira, 2004; Thoa et al., 2017; Trọng et al., 2013a) and

most may be species specific and also depend on the actual pairs of traits examined.

The phenotypic correlation between WAS and NE was moderate (0.45), a result

that is comparable with other estimates for P. vannamei with rp 0.34 (Arcos et al.,

2004) and 0.27 (Caballero‐Zamora et al., 2015). A relatively high rp between WAS

and NE has also been reported in some other penaeid species (Macbeth et al., 2007;

Tan et al., 2017b). The correlation between WAS and NN was lower than between

WAS and NE, but was still moderate and positive (see Figure 5.1 and Figure 5.2). A

lower rp for WAS and NN compared with that for WAS and NE may result from data

with several spawning females showing a zero NN record; this phenomenon usually

results from successfully mated females losing their adhered spermatophore during

the spawning period. Comparisons of data from reproductive studies of penaeid taxa

show that P. vannamei belongs in a group of species with an open thelycum (a

secondary sexual character involved in sperm transfer and storage in females), that

have always been observed to show a small percentage of spawns that do not result

in hatched nauplii. This results from otherwise successfully mated females losing


their adhered spermatophore before mature eggs can be released. In this study, the

phenotypic correlations between WAS and other reproductive traits were also all

positive which indicates that larger females in general produce more spawns, release

a larger number of fertilized eggs and produce a larger number of nauplii than do

smaller females (Ibarra et al., 2007b; Palacios et al., 1999a). Therefore as a general

rule, large body size for mature females is a good practical and measurable

characteristic for inferring likely relative reproductive performance of individual

female P. vannamei broodstook.

Importantly, estimates for phenotypic correlations were always close to that of

genetic correlations for the traits studied here, a result that supports Cheverud’s

conjecture that phenotypic correlations could be used as a proxy for genetic

correlations (Cheverud, 1988). Whilst there has been some debate about this, notably

by Willis et al., (1991), this inferred relationship has been supported in a number of

studies in insects (Reusch and Blanckenhorn, 1998; Roff, 1995), tamarins (Rogers

Ackermann and Cheverud, 2002), plants (Waitt and Levin, 1998), and even humans

(Sodini et al., 2018). In addition, positive relationships between phenotypic and

genotypic correlations were more often concordant for morphological traits,

compared with behavioural or life history traits (Kruuk et al., 2008; Roff, 1996).

5.4.4 Implication for Selection Programs

Improving reproductive performance for Pacific white shrimp strains is a key

objective for broodstock marketing in the future. Because fresh, nutritional food for

broodstock maturation is a major financial constraint on hatcheries, the nauplii

production sector would benefit significantly from strains with improved

reproductive output, either by maintaining smaller broodstock populations to achieve


the same seed production or investing the same resources but producing more seed.

Both ways would result in a significant increase in hatchery profit. The capacity to

improve nauplii production from each female broodstock individual has huge

potential, with the possibility that spawning frequency could be trebled. Breeders can

also include reproductive traits along with current selected traits via either a multiple

trait selection approach or a two stage selection approach. Additionally, a genome

selection (GS) approach has been successfully applied to improve total egg

production in poultry science recently, so GS is a direction that could also be trialled

to improve reproductive traits in shrimp. Results may also be useful for genetic

improvement of black tiger shrimp (P. monodon), as this species has a similar

reproductive cycle to that of Pacific white shrimp and there are still major difficulties

associated with nauplii production in black tiger shrimp farming.


5.5 CONCLUSIONS

Results from the current study clearly demonstrate and confirm that additive

genetic variation exists for an important set of female reproductive performance traits

including: number of eggs per spawn, number of nauplii per spawn and multiple

spawning capacity in our domesticated Pacific white shrimp line in China and so

these traits likely can be improved via genetic selection. In contrast, limited additive

genetic variation was also evident for some other reproductive performance traits

notably: egg hatching rate and the relative fecundity per weight (g) of individual

broodstock females, so these two latter traits are unlikely to be improved via genetic

selection. Evidence for both positive and moderate genetic correlations and

phenotypic correlations between body weight after spawning and some reproductive

traits also suggests that reproductive performance of female broodstock following

selection to increase mean body weight would not be compromised. It is clear

however, that data will need to be collected over future multiple generations of

selection from our domesticated line to investigate any potential for deterioration in

reproduction performance due to the cooperative impacts of selection for improved

mean body weight and accumulation of inbreeding over time.

166 General Discussion

Chapter 6: GENERALDISCUSSION

Food security is a major challenge for modern human societies, notably the ability

to produce sufficient food for a growing global population, which is expected to

increase to more than 9 billion by 2050 (Béné et al., 2015; FAO, 2018b). While wild

capture fisheries are declining and agriculture production is plateauing, aquaculture

has maintained the highest production growth rate of all food commodities over the

last 60 years around the world (FAO, 2018a). In 2014, production from aquaculture

overtook that of wild caught fish for human consumption. Aquaculture will also

become the largest sector in the meat industry contributing animal protein for human

consumption, passing traditional terrestrial meat production in the next decade (FAO,

2018b). Aquaculture therefore, will play an increasingly important role for securing

global food security (FAO, 2018b; Gjedrem and Robinson, 2014; Gjedrem et al.,

2012).

There is huge potential to increase total aquaculture production around the world

by implementing more stock improvement programs. While most production of

agriculture species is now based on selective breeding, it is estimated that only 10%

of global aquaculture production comes currently from selectively bred stocks

(Gjedrem et al., 2012).

The deficiency of high performance broodstock is an urgent issue for the

sustainability of Pacific white shrimp farming in China. Specifically, prawn farming

is facing a number of serious challenges: 1) imported SPF broodstock are more

susceptible to many local pathogens and diseases resulting in a low pond survival

rate in farms across Asia (Cock et al., 2017; Cock et al., 2009; Moss et al., 2012a); 2)

some hatcheries directly source broodstock from local culture ponds, without any

General Discussion 167

genetic management creating issues associated with inbreeding (Doyle, 2016;

Thitamadee et al., 2016); 3) and to produce a base population with broad genetic

variation will be a challenge due to a lack of available wild resources. The most

viable solution to address these problems is for the shrimp farming industry to design

better breeding programs and to develop locally adapted strains that show high

survival and improved growth rates that will generate greater profits while targeting

the specific farm and market conditions present in China. The main objective of the

current project therefore was to assist the design of future breeding programs for

marine shrimp that seek to develop locally adapted strains targeting specific farming

and market conditions in China. Here I discuss the results and implications that

resulted from the study and identify future directions for research.

6.1 CHARACTERIZATION OF PACIFIC WHITE SHRIMP GENETIC

DIVERSITY AND GENETIC STRUCTURE IN CHINA

The main outcome from this component of the study was strong genetic evidence

(pairwise FST results, phylogenetic relationships among stocks, and Bayesian

structure assignments) that modern Pacific white shrimp hatchery lines in China have

come from different genetic backgrounds and the patterns of genetic differentiation

observed among the lines were consistent with the historical records of introductions

of this exotic species into China. The main application of this finding in the current

project was that the data provided a foundation for selecting a subset of genetically-

diverse populations to be trialled in a diallel cross to produce a base family line.

Knowledge of genetic differentiation patterns among different culture lines in China

can also inform development of a sustainable conservation management plan for

Pacific white shrimp breeding lines. A major highlight of the genetic diversity results

was the demonstration that Chinese nauplii hatcheries, in general run by small


entrepreneurs, have conserved significant levels of genetic diversity across many

generations since introduction as a result of the high Ne actively employed when

choosing broodstock for nauplii production. This practice, while not unique among

regions of the world where Pacific white shrimp are farmed, is relatively uncommon

in small hatcheries because of the greater costs, facility implications and time

required to maintain large numbers of broodstock individuals.

A third aim of the study was to estimate the genetic relatedness among sampled

breeding lines available in China as, prior to this study, virtually no data had been

recorded formally about where stocks had been sourced and if they potentially reflect

multiple representations of the same genetic resource or not. Results show that cross

breeding four sub-populations could control for the potential risk associated with

high inbreeding levels. This outcome also shows clearly that the breeding lines

sampled in this study have been kept isolated from each other across many

generations of domestication and farming in China.

Potential future work in this area could investigate the underlying factors

influencing the observed population structure of domesticated Pacific white shrimp

genetic resources in China, relating this to potential sources of wild stocks in the

Americas that were used to found these lines. This would necessitate however, a

regional assessment of wild genetic resources across the Pacific coasts of northern

Mexico, Central America and northern South America to establish the natural

phylogeographic patterns of variation across the entire natural range of the species.

Given the importance of this species in aquaculture, it is surprising that this analysis

has not already been undertaken. Patterns of variation in domesticated aquatic

animals, in particular those species of significant commercial interest, will be

impacted by both natural patterns of biological differentiation and anthropogenic


impacts on domesticated populations that shape and result in often complex patterns

of genetic structure in captive populations (Bruford et al., 2003; Mignon-Grasteau et

al., 2005; Zenger et al., 2007). While to date, only very limited data is available from

earlier studies that have investigated the natural population structure of Pacific white

shrimp (Valles-Jimenez et al., 2004; Valles-Jimenez et al., 2006), these studies have

suggested that wild populations may be structured spatially. Potential barriers to gene

flow among geographically-dispersed populations can result from a diverse

combination of physical, oceanographic, and biological factors (including different

breeding seasons) (Valles-Jimenez et al., 2006). The natural population structure of

Pacific white shrimp still remains unresolved, largely due to the fact that no

comprehensive wild population sampling scheme has been undertaken. Due to its

important role in global shrimp farming, a major study designed to determine the

natural population genetic structure of Pacific white shrimp is definitely warranted.

While knowledge of available genetic resources is central to a well-designed

breeding program, traditional methods for estimating genetic variation in wild

populations are rapidly being replaced by next generation sequencing technologies

that can provide greater resolution of variation relevant to genetic improvement in

aquaculture species. Domestication of Pacific white shrimp for farming has only

occurred in recent decades in contrast to the process in terrestrial domesticated farm

animals that began over ten thousand years ago (Driscoll et al., 2009; Frantz et al.,

2015; Gjedrem et al., 2012). Progress with domestication of Pacific white shrimp

therefore can provide a model species for assessing the genetic changes that have

happened during the initial steps of the animal domestication process. Recently, a

number of genome-wide analyses have identified the genetic signatures of the

domestication process in several terrestrial farm species (Alberto et al., 2018;


Carneiro et al., 2014; Frantz et al., 2015; Rubin et al., 2010). In the future,

availability of a whole genome sequence for domesticated Pacific white shrimp

strains and their wild counterpart populations can identify the genomic signatures of

domestication of this important aquatic farmed species (Zhang et al., 2019).

6.2 GENETIC PARAMETERS FOR BODYWEIGHT AND SURVIVAL IN

THE BASE POPULATION

In Chapter 3, I investigated how much genetic variation in growth traits had been

captured during development of our base population of Pacific white shrimp for

genetic improvement. These results can benefit current knowledge about how best to

form foundation populations during genetic improvement of other farmed aquatic

species. In fact, the approach for developing penaeid shrimp stocks for genetic

improvement programs have varied significantly between the “West” (Americas) and

the “East” (elsewhere); in part this has resulted from the extent of this species’

natural distribution. In the East, broodstocks have commonly been sourced directly

from wild populations, while the majority of culture industries in the West are based

on domesticated farm strains with genotype/pedigree information applied where it is

available (Boyd et al., 2006).

In the current study, quantitative data from 89 full-sib families were analysed

using a univariate animal model following REML methodology. High heritability

estimates for growth traits confirmed that a substantial component of additive genetic

variance (BW1: h2 = 0.52 ± 0.09; BW2: h2 = 0.44 ± 0.07) was available for growth in

our base population prior to imposing a family selection program to improve strain

productivity. In comparison, reports on heritability of growth traits of penaeid shrimp


in commercial environments (farmed ponds) range from 0.15 to 0.4 (Campos-Montes

et al., 2013; Campos-Montes et al., 2017; Castillo-Juárez et al., 2007; Gitterle et al.,

2005c; Ibarra and Famula, 2008b; Krishna et al., 2011; Nolasco-Alzaga et al., 2018;

Pérez‐Rostro and Ibarra, 2003a; Sui et al., 2016a; Sui et al., 2016b; Sun et al., 2015a;

Zhang et al., 2017). Recognition of high heritability for growth traits in the current

study are consistent with other reports of penaeid shrimps tested in recirculating

tanks, where h2 ranged from 0.23 to 0.84 with a mean >0.4 (Argue et al., 2002;

Coman et al., 2010; Kenway et al., 2006; Macbeth et al., 2007). We also identified

substantial differences in growth and survival rates among the strains tested under

our production conditions.

Future work may include designing and implementing a breed improvement

strategy to exploit the significant differences in mean growth rates among strains in

order to maintain optimum levels of genetic variation across generations while

achieving appropriate genetic gains. Furthermore, results of base strain comparisons

can be applied directly to improving productivity of existing shrimp farming in

China. Performance for growth and survival rate of different strains often differ in

different farming environments. Thus, it will be beneficial to undertake strain tests in

different real farming conditions in China in the future to identify the best

performing candidate strains for different sites and conditions.

6.3 COMPARISON OF REPRODUCTIVE PERFORMANCE OF FEMALE

PACIFIC WHITE SHRIMP REARED IN RECIRCULATING TANKS

VS EARTHEN PONDS

Running any efficient genetic improvement program requires a large amount of

labour and capital investment while producing a low cost to high benefit ratio,

particularly for breeding of aquatic species. A key initial factor to consider before


making a decision to initiate a breed improvement program is to first evaluate

whether the existing husbandry and management practices have been optimised. In

Chapter 4, I compared the two best potential approaches (RT and EP) typically used

worldwide for rearing Pacific white shrimp broodstock to optimise their reproductive

performance. The main objective was to optimise the quality of reproductive

performance of candidate female broodstock, and to facilitate development of an

effective seed dissemination strategy.

The approach adopted was to use nauplii from a single batch and evaluate their

relative performance experimentally in either recirculating tanks (RT) or earthen

ponds (EP). This approach eliminated any potential impact from the genetic resource

used or age of experimental animals. Initially, we confirmed that both rearing

approaches (RT and EP) could be used successfully to rear broodstock as essentially,

there were no significant differences in reproductive parameters assessed in the study

except for female spawning frequency. Evidence here supports the untested claims

by technicians in Chinese shrimp hatcheries that broodstock females from earthen

ponds were easier to mature and showed a higher mating frequency compared with

SPF females (usually reared in tanks). EP-reared females showed a significantly

higher spawning frequency compared with RT-reared females across a 30 day

experimental trial (1.93 vs 1.34). When reproductive performance was evaluated

against body size, different correlations were evident for females between the RT and

EP treatments. While large sized RT-reared females showed a high mean spawning

frequency, medium-sized females in the EP treatment showed double the spawning

frequency compared with small and large sized EP females. The results of

comparative female reproductive performance in relation to individual spawning


order confirmed that multiple spawning by females is a desirable trait with no

evidence detected for a decline in egg quality in multiple spawners.

Overall, results from this study largely resolved the husbandry and management

practices required to initiate a genetic improvement program for Pacific white shrimp.

Recirculating tank systems can provide the necessary high quality water environment

in an indoor bio-secure environment that does not compromise reproductive

performance while also providing the required security to protect important live

genetic resources in the breeding nucleus while preserving accumulated genetic gains

in live animals across generations. These results provide strong evidence that female

reproduction quality in an RT environment can meet breeding program goals.

In parallel, this development allowed for an effective strategy for future seed

dissemination of the breeding line. The practice of rearing sufficient broodstock to

meet the requirements of multipliers is a high risk venture in open pond

environments, but is favoured because of the relatively low costs and reduced

management requirements while potentially resulting in good broodstock

reproductive performance. Open ponds can also be used as backup facilities for

maintaining breeding lines reducing the need for more expensive RT systems.

Future work may benefit from optimising Pacific white shrimp female

reproduction biology via developing and applying RNA interference technology

(Feijó et al., 2016; Treerattrakool et al., 2011; Treerattrakool et al., 2008) and/or

applying gonad-inhibiting hormone supplementation (Sathapondecha and Chotigeat,

2018; Treerattrakool et al., 2014; Vrinda et al., 2017) to induce ovarian maturation.

In shrimp farming, unilateral eyestalk ablation is currently considered to be the most

effective technique used to induce ovarian maturation on demand in penaeid shrimps.

The eyestalk in crustaceans is the location of the X-organ-sinus gland complex which


is the site of synthesis and storage site of gonad-inhibiting (GIH)/vitellogenesis-

inhibiting hormone (VIH) (Wilder et al., 2010). GIH plays an inhibitory role in

ovarian maturation by inhibiting vitellogenin (Vg) synthesis (Feijó et al., 2016;

Sathapondecha and Chotigeat, 2018). Eyestalk ablation results in a reduction in GIH

and as a consequence stimulates ovarian maturation in mature females. This method

however, is invasive and causes stress following surgery to females potentially

increasing mortality rates. An alternative to eyestalk ablation is to use RNAi to

silence the GIH gene. This approach is now considered to be a practical alternative

for inducing reproduction in captive penaeid shrimps. This technology has shown

excellent potential for silencing hormonal gene transcripts in Pacific white shrimp

(Feijó et al., 2016) and black tiger shrimp (Das et al., 2015; Treerattrakool et al.,

2011). Further studies on endocrine regulatory mechanisms will be necessary

however, to develop an efficient and cost effective RNAi methodology to replace the

traditional method of eyestalk ablation so that it can be applied routinely in shrimp

hatcheries.

6.4 QUANTITATIVE GENETIC ANALYSIS OF FEMALE

REPRODUCTIVE TRAITS

The final set of studies in the current project investigated reproductive traits in

females that are among the most significant commercial factors affecting nauplii

production in hatcheries and addressed the following questions (i) can we improve

female reproductive traits via a genetic selection approach, and (ii) does selecting for

improved body weight in females produce any potentially negative effects on

broodstock reproductive quality?

A significant amount of additive genetic variance was identified for a number of

key reproductive traits in mature females (number of eggs per spawn, number of


nauplii per spawn, and spawning frequency) that could potentially be exploited in a

stock improvement program. In contrast, only limited additive genetic variance was

identified for hatching rate of eggs and number of eggs produced relative to

individual weight (g) of female broodstock. This suggests that the last two traits are

unlikely to be improved via a genetic selection approach. Results for genetic

correlations between body weight after spawning and female reproductive traits also

confirmed that there was no evidence that selecting for higher mean body weight

would produce any negative effects on female broodstock reproductive quality. One

interesting outcome was that h2 for female body weight after spawning (WAS) was

extremely high (0.68 ± 0.10), a result consistent with recent published studies on

other farmed aquatic species (Thoa et al., 2017; Trọng et al., 2013a). This implies

that heritability for the growth trait of body weight at spawning time is almost 2~3

time higher than most published results on heritability of body weight at harvest

stage (130 days – 150 days post farming) (see Chapter 1 Introduction on reviews of

genetic parameters of growth traits), which will result in at least a doubling of

genetic gain for the fast growth trait by selecting at maturation stage rather than at

harvest stage. Applying this information to breed improvement projects on penaeid

shrimps however, depends on the genetic correlations between body weights at these

two stages. In animal breeding, growth traits between different life stages are

considered as different traits; if the genetic correlation for body weight between

harvest and maturation stages is high, the optimal strategy for selection for fast

growth in shrimps will focus on the maturation stage.

In the future, data will need to be collected over multiple generations of selection

from our domesticated line to assess whether female reproductive performance

declines due to the interactive effects of impacts of selection for improved mean


female body weight and accumulation of inbreeding over time. In addition,

reproductive traits related to the success of larval culture (i.e. metamorphosis rate to

Z1, metamorphosis index to M1, and survival rate to PL1) should also be examined

to characterise levels of available additive genetic variance for these important traits.

6.5 FUTURE DIRECTION FOR PACIFIC WHITE SHRIMP BREEDINGPROGRAMS

6.5.1 Breeding Strain for AHPND Disease Resistance

The break out AHPND disease has become the largest modern challenge for

shrimp farming and has resulted in huge economic losses in Asia, Mexico and South

America (Dash et al., 2017; Nunan et al., 2014; Thitamadee et al., 2016). This

disease is caused by opportunistic pathogens from six Vibrio species and usually

occurs at ~35 days after stocking of shrimp PL (Devadas et al., 2018). AHPND has

recently caused huge farm losses, with mortality rates between 40% and 100% (Hong

et al., 2016). In the Asian shrimp farming industry, production losses caused by

AHPHD have been estimated at more than USD 1 billion annually since 2012

(Reantaso et al., 2013). From the perspective of health management practices,

currently, we can neither exclude the disease agent from the shrimp farming

environment nor find effective strategies to combat this major shrimp disease.

Alternatively, developing an AHPND disease resistant strain can be a solution to

disease control. Toward this goal, setting up a challenge test model and quantifying

additive genetic variance in the population for resistance/tolerance will be the first

step towards genetic breeding of an AHPND resistant strain.

From past experience with genetic breeding for disease resistance in penaeid

shrimp, we tend to be excessively pessimistic about the challenge test model

approach. Generally, disease resistance is a fitness trait that shows very low


heritability (Cock et al., 2017; Cock et al., 2009; Sae‐Lim et al., 2016). The only

successful case of genetic breeding for resistance in P. vannamei is with TSV where

there was a report of moderate heritability (Argue et al., 2002; White et al., 2002) for

this trait. However, the story behind this case is not straight forward. This study was

undertaken at the Oceanic Institute in Hawaii, where the additive genetic variance on

TSV resistance of local populations was low. An Ecuadorean population however,

that was developed from survivors of earthen ponds where TSV was present, showed

large additive genetic variance for TSV resistance (Cock et al., 2009). That is to say,

TSV resistance was most likely obtained via natural selection via co-evolution of P.

vannamei and the TSV virus in earthen ponds in Ecuador.

Most disease resistance traits in aquaculture are reported to result from polygenic

factors which means the phenotype is determined by input from many gene loci

(Barría et al., 2018; Correa et al., 2015; Yáñez et al., 2019). Therefore,

implementation of a genome selection (GS) approach is likely to be more effective

for estimating marker effects than conventional methods based on simple challenge

test models (Goddard et al. 2009; Fernando & Garrick 2013).

6.5.2 Genome Selection (GS)

Disease is a major constraint on the shrimp farming industry worldwide (Devadas

et al., 2018; Lightner, 2011). Comparing the two major aquaculture farming

industries of Pacific white shrimp and Atlantic salmon, vaccination has made a

significant contribution to combating disease in the salmon industry (Brudeseth et al.,

2013; Salgado-Miranda et al., 2013), but it is an ineffective management strategy in

shrimp farming because invertebrates lack an adaptive immune system (Hauton,

2012; Rowley and Pope, 2012). Currently, most shrimp breeding programs directed

at disease threats set challenge tests to exploit additive genetic variance for disease


resistance between and within families. From the perspective of quantitative genetics,

this method is problematic because most traits associated with disease resistance are

complex traits controlled by many genes, usually showing low heritability (Cock et

al., 2009; Gitterle et al., 2005a). Genome selection could enhance breeding accuracy

compared with conventional selection methodologies, as reported for other complex

quantitative traits, for example milk production in cattle where genetic gains have

doubled production (Hickey et al., 2017b; Wiggans et al., 2017). GS has recently

overtaken conventional selection in terrestrial animals, because accuracy of breeding

values are improved, enhancing genetic gains, and shortening generation times

(Hayes et al., 2009). Application of GS to shrimp production improvement however,

still suffers from two major constraints, namely a deficiency in available genetic

resources coupled with the high cost of genotyping. In addition, high density (HD)

SNP chips are a fundamental tool for an effective GS approach, but currently they

are not readily available for P. vannamei. The development of HD SNP chips

requires genome information on the target species and high quality genome

assemblies. At the time of writing this thesis, the first draft genome sequence for

Pacific white shrimp was published, covering only 1.66 GB (~65%) of the complete

genome (Zhang et al., 2019). In the future, development of a more complete map for

the P. vannamei genome will be critical. The second constraint, cost of genotyping,

is expected to be resolved soon due to rapid technological advances in the field, and

should not be a factor limiting application of GS to production improvement of

penaeid shrimps in the near future.

6.5.3 Dissemination of the Improved Pacific White Shrimp Stock

The seed dissemination strategy for breeding programs in Pacific white shrimp is

quite simple as female fecundity is very high (~1, 000, 000 nauplii per female).


Therefore, if the breed improvement project is targeting a small market, a

multiplication hatchery would not be necessary; just dissemination of the post larvae

(PLs) or nauplii to the market simultaneously during each new breeding cycle would

be sufficient. Commercial models for most Pacific white shrimp breeding programs

however, sell broodstock to the international market to authorized nauplii hatcheries.

That is, in each breeding cycle, while breeding the nucleus lines to maintain high

genetic variation levels, the best ranking families are crossed to produce marketed

broodstock with optimised growth performance while neglecting levels of inbreeding.

Some top Pacific white shrimp breeding programs have trialled a fully centralized

model that only supplies nauplii and PLs to the international market as broodstock

from this project have overwhelmingly faster growth performance than other strains.

Depending on the market niche, the first two commercial models offer better seed

dissemination strategies for a Pacific white shrimp breeding program.

6.5.4 Further Application of Current Project

Lessons from this project can also be applied to genetic improvement of other

aquaculture species. Efficiently managing pedigrees and capturing broad genetic

variance are the key factors for the success of genetic improvement programs on

aquaculture species. This was clearly demonstrated by the first family-based

selection program in an aquaculture species; Atlantic salmon in Norway in 1975

(Gjedrem, 2010). Conversely, a number of well-organized breeding programs for

aquatic species have ultimately failed because exploitable levels of genetic variation

in the base population were initially low or declined rapidly over a few generations

(Huang and Liao, 1990; Teichert-Coddington and Smitherman, 1988). In the current

project, genotype information generated from molecular markers for domesticated

strains of P. vannamei in China demonstratively provide a fundamental tool for


producing a healthy base population for future genetic selection programs, that

maximize genetic variance while controlling for inbreeding. Further quantitative data

on growth traits clearly demonstrate that this approach can work effectively. Exotic

aquaculture species make significant contributions to aquaculture production

worldwide. By estimating genetic differentiation and genetic relatedness for taxa that

are already domestically available, instead of sourcing broodstock from wild

populations, we can instigate genetic improvement programs that can promote

biosecurity, while still being economically successful and sustainable over the long

term.


6.6 CONCLUDING THOUGHTS

Pacific white shrimp has become the foremost farmed aquatic species and food

commodity in terms of trade value in world aquaculture. As this change has occurred,

China at the same time has become the largest farmed shrimp producer across the

world. Sustainability of the farmed shrimp industry in China however, now faces

significant challenges, in particular that pond survival of the farm strains in use

currently, is generally very low. This problem offers an opportunity for industries to

design better breeding programs and to develop locally adapted strains that show

high survival and good growth rates that will generate greater profits while targeting

specific farming and market conditions in China.

As an important step towards the development of the Pacific white shrimp

farming industry in China, the current study has, (1.) developed a set of robust

affordable molecular tools that can be used to assess genetic diversity, population

structure, and genetic relatedness in domesticated culture resources of Pacific white

shrimp in China and (2.) generated quantitative genetic data that suggests that

synthesising a base population from domesticated strains applying a “genotypic

approach” can capture broad genetic variation for growth that can theoretically, be

exploited successfully via selection in a future breed improvement program.

Husbandry and management practices continue to constitute key difficulties that

impact development of efficient genetic improvement programs for farmed penaeid

species. Protocols developed and trialled (3.) in Chapter 4 as part of a reproductive

performance study of Pacific white shrimp females here, we believe can at least in

part, address the problems identified above. Application of this information will not

only be crucial for the success of developing better genetic nucleus lines in China,


but also when making decisions in the future on dissemination of seed from genetic

lines to the markets. Finally, (4.) quantitative genetic analysis of important female

reproductive traits suggests that a number of key reproductive traits in Pacific white

shrimp female broodstock can be improved by a genetic selection approach. It will

be important however, to design breeding plans to maintain an appropriate balance

between achieving genetic gains across generations while conserving and managing

levels of exploitable genetic variation in improved lines.

References 183

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Appendices 239

Appendices

Appendix A

Supplementary Figure S2.1

Figure S2.1 The estimated delta values illustrate the most likely number ofsubpopulations (K = 2 and K = 4) based on Bayesian assignment.

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