Aquaculture 428–429 (2014) 71–78

Contents lists available at ScienceDirect

Aquaculture

j ourna l homepage: www.e lsev ie r .com/ locate /aqua-on l ine

Can genetic diversity be maintained in long term mass selectedpopulations without pedigree information? — A case study usingbanana shrimp Fenneropenaeus merguiensis

Wayne Knibb a,⁎, Paul Whatmore a,b, Rob Lamont a, Jane Quinn a,Dan Powell a, Abigail Elizur a, Trevor Anderson c, Courtney Remilton c, Nguyen Hong Nguyen a

a The University of the Sunshine Coast, Maroochydore, Queensland 4558, Australiab The Australian Seafood Cooperative Research Centre, Bedford Park, SA, Australiac Seafarm, Bruce Hwy, Cardwell, QLD 4849, Australia

⁎ Corresponding author at: Faculty of Science Health aSunshine Coast, Locked Bag 4,MaroochydoreDc, QLD 4558,

E-mail address: wknibb@usc.edu.au (W. Knibb).

http://dx.doi.org/10.1016/j.aquaculture.2014.02.0260044-8486/© 2014 Elsevier B.V. All rights reserved.

a b s t r a c t

a r t i c l e i n f o

Article history:Received 3 December 2013Received in revised form 20 February 2014Accepted 24 February 2014Available online 4 March 2014

Keywords:Genetic diversityInbreedingLoss of genetic variabilityShrimpMass selection

This study assesses the changes in genetic diversity using two different methods in eight captive bred lines ofbanana shrimp (Fenneropenaeus merguiensis) that had been mass selected for length for up to 14 generations.Specifically, mitochondrial D-loop DNA sequencing and genotyping using five DNAmicrosatellite loci were usedto document changes in haplotype diversity and allelic diversity numbers at several time points during and up to14 generations of captive bred lines, typically maintained without intercrossing among lines. Data from eight ofthe lines were compared with each other and to a reference sample of wild caught animals.As eachwild animal had a uniquemtDNAhaplotype,we estimate that therewere 20 differentmtDNAhaplotypesin each of the two different founding stocks. The average number of haplotypes was 1.8 after 11 or moregenerations of captive breeding. Similarly, whereas the wild reference stock had an average number of morethan 13 microsatellite alleles per locus, the descendent lines had an average of 5.6 per locus after 11 or moregenerations. These declines were evident despite strategies that had been put in place to maintain geneticvariation, including the use of up to 1000 brood stock per generation. The loss of genetic variation was unequivocalbeing evident for both DNA methods and in all the different lines.Effective population size (Ne), as derived from linkage disequilibrium, was estimated to average about nine after11–13 generations in the captively bred lines, compared with 263 estimated in the wild samples. Thiscorresponds to a rate of inbreeding of about 4% per generation for the captively bred lines. Additive geneticvariance of the captively bred lines, estimated under the assumption of neutrality, ranged from about 75% to25% that in the wild samples.We therefore conclude that mass selection, evenwhen using a relatively large number of broodstock, still resultsin substantial loss of allelic diversity within lines over generations, and a reduction of effective population sizeand genetic variance, to the degree that productivity could have been compromised comparedwith similarly se-lected but outbred stocks. Loss occurred relatively consistently among the different lines.It was common for different microsatellite alleles or mtDNA haplotypes to have persisted in the different lines,such that the total number of haplotypes and allele types among all lines was much greater than that withingiven single lines, and the number of alleles among lines approximated that found in the wild. This observation,evident because many different lines were monitored, suggests that under certain circumstances (fixation andselection), more net genetic variability can be maintained over many generations of selection by keeping multi-ple different and independent lines rather than one large single line. Accordingly, ifmultiple lines aremaintained,there could be some practical options to reconstitute allelic and haplotype variationwithout new introductions ofgenetically unimproved stock from the wild.

© 2014 Elsevier B.V. All rights reserved.

nd Education, University of theAustralia. Tel.:+61 7 5430 2831.

1. Introduction

The management and maintenance of genetic diversity in selectionlines of captive bred stock without reintroductions from the wild is amajor issue for selection programmes, particularly for fecund aquacul-ture organisms where the offspring of relatively few brood stock can

72 W. Knibb et al. / Aquaculture 428–429 (2014) 71–78

form the breeders of the next generation. High fecundity, coupled withsmall population sizes, variable fertility of families and selection thatfavours only a few families can promote inbreeding and loss of geneticvariation (Knibb, 2000). Various studies report the loss of allelicvariation over generations in aquacultured organisms under selectionof closed populations (Loughnan et al., 2013; Loukovitis et al., 2012;Vela Avitúa et al., 2013). Loss of genetic variation can be accompaniedby inbreeding and inbreeding depression, with concordant loss ofperformance, fitness and selection response (Evans et al., 2004;Kincaid, 1983; Wang et al., 2001).

It is now widely understood in many aquaculture industries that aneffective method to manage and maintain genetic diversity is to havepedigrees built from either physical tagging (after a period of separaterearing) (Nguyen et al., 2010) or from identification using DNAmarkerssuch as DNAmicrosatellites (Kinghorn, 2011). To these pedigrees, infor-mation about kinship relationships can also be used to choose matesand further reduce loss of variation (Meglécz et al., 2010). Of coursethere are other advantages of pedigrees, such as enhanced selectionresponse, although this study focuses on issues of inbreeding. Positive re-sponse to selection has been demonstrated in aquaculture programmeswhere tagging and pedigree construction were completed (Hung et al.,2013), and conversely, selection response was poor in mass selectionprogrammes that didn't use pedigree information (Bentsen and Olesen,2002).

However, there are economic, logistical and infrastructure costs aswell as technical challenges such as tagging small animals associatedwithmanaging full pedigrees, and there are numerous genetic selectionprogrammes in aquaculture still based on some type of mass selection,without pedigree information (Nguyen pers. comm.). At issue is howto best preserve genetic variation in mass selected lines without pedi-gree information. Are there any variations on mass selection thatmaintain genetic variation without the costs of maintaining a pedigree,or permit reconstitution of lost genetic variationwithout new introduc-tions from the wild?

An example of multigenerational mass selection without pedigreeinformation exists for banana shrimp (Fenneropenaeus merguiensis)farming in Australia. This sector commenced mass selection of shrimpfor size in 2000 and has conducted selection on different captivelybred lines for up to 14 generations. What is relatively unique with thisprogramme is that many (8) relatively independent captive lines weremaintained, which afford the opportunity to first evaluate the consis-tency of the pattern of genetic change over generations and second, toassess if splitting the stock into various lines effectively conservesgenetic variation for the whole population. Various steps were under-taken to minimise loss of variation and inbreeding under commercialoperating conditions, including the use of 40 wild founding individuals(on two different occasions), breeding from up to 1000 individuals eachgeneration, selecting candidate broodstock from different growoutponds, selecting the sexes from different growout ponds, andmaintain-ing multiple independent selection lines.

Here we assess both mtDNA haplotype and DNA microsatellitevariation at different generations within and among these captivelybred and selected banana shrimp lines and compare it to that in wildsamples. Effective population sizes and loss of additive genetic variationare estimated from allelic diversity data. In light of the results, thepossibility of reconstituting genetic variation by crossing betweenlines is considered as a general model or approach for aquaculturemass selection programmes.

2. Materials and methods

2.1. Location of animals

All shrimp sampled, except for wild animals, were taken from theSeafarm farm site at Cardwell, Australia (latitude 18° 16′ 0S, longitude146° 1′ 60E, altitude 0 m). The daily average temperature in Cardwell

is between 14 °C and 32 °C, with the minimum average of 19 °C andthemaximumaverage of 29 °C over the last 103 years (Australian Bureauof Meteorology, 2012). The water temperature in cultured ponds variesbetween 25 °C and 32 °C. The annual rainfall is 2129 mm, occurringmainly from December to April with a peak in January, February andMarch.

2.2. Lines

In 2000, twenty wild inseminated females were used to initiate thefirst lot of captive stocks. Over the next four generations, six lines wereproduced by spitting and crossing, and these were on-bred for 10 ormore generations (Fig. 1). Four of the six were never crossed withother lines. At generation 9 of the above lines, two new lines wereinitiated from 20 new inseminated wild females and the new lineswere maintained for five generations (Fig. 1).

2.3. Selection and breeding cycle

For a given generation and a given line, the typical breeding cyclecomprised: a. selection of the top 7% (by linear length) of femaleswhen they reached harvest size (25 g) from four 1.1 to 1.7 ha ponds,and selection of the top 7% of males from each of two 1.1 to 1.7 haponds, b. transport of 700 males and 1400 females to the hatcheryfarm (Flying Fish Point) and ongrowing in small 0.1 ha ponds until sex-ually mature (usually at 40–60 g), c. transfer of animals to 100 tonnerectangular concrete tanks, d. feeding with maturation diets of frozensquid, polychaete worms, artemia biomass and maturation pellets, e.up to 1000 broodstock females per breeding group per generationwere used by sequentially spawning over one to two nights lots of 20–40 eye stalk ablated females in 10 tonne tanks, f. mixing of all PLs fromthe 20–40 dams to stock one pond, and for the total 1000 females ofthe generation, stocking of about 15–20 commercial ponds at Cardwell(each female averages 40,000 nauplii).

2.4. Sampling

Pleopods and/or eye stalks were sampled from various lines fromgeneration 3 (post establishment from one of two wild stocks) untilgeneration 14, and details of sampling times and sampled lines areavailable in Fig. 1. Tissue samples were also taken from 54 wild shrimpcollected from the same location as the original wild broodstock used toinitiate the Seafarm lines. All samples were preserved in 70% ethanoland shipped to the University of the Sunshine Coast (USC).

For the DNA microsatellite analyses, the minimum sample size was24 individuals for a given line and given time, the maximum was 200,although for the statistical analysis we restricted each of the 16 line/time data sets to 24 samples (genotype data given in SupplementaryTable 1). For the mtDNA component, sample sizes ranged from 15 to200, although for statistical analysis, each of the 15 time/line data setswas restricted to 15 samples (Supplementary Table 2). The number ofhaplotypes, for a given line, from the smaller samples was reflective ofthose from the larger samples. Sequences of the haplotypes are givenin Supplementary Table 4.

2.5. DNA extraction

Total genomic DNA for mtDNA and microsatellite analyses wasextracted from either eye stalk or pleopod tissue of all individualsusing the DNeasy 96 Blood and Tissue Kit (Qiagen; Hilden, Germany)as per the manufacturer's instructions. Purity and concentration wasconfirmed using a NanoDrop 2000c Spectrophotometer (Thermo FisherScientific, Brisbane).

Cohorts over generations

= genotyped for five loci

= sequenced for mtDNA haplotypes

G12EG12D

wild founder collection 1

G1A G1C

G2A G2B G2E G2F

G3A G3B G3D G3E G3G

G4A G4B G4D G4E G4F G4G

G5A G5B G5D G5E G5F G5G

G6A G6B G6D G6E G6F G6G

G7A G7B G7D G7E G7F G7G

G8A G8B G8D G8E G8F G8G

G9A G9B G9D G9E G9F

G10A G10B G10D G10E G10F

G11E G11FG11DG11A G11B

G9G

G12F

G13FG13EG13D

G10G

G12BG12A

G13A

G14E

G11G

wild founder collection 2

G2H

G1H

G3H

G4H

G1I

G2I

G3I

G4I

G15I

Progeny

Fig. 1. Diagrammatic illustration of the 8 lines, times of crossing and of sampling for mtDNA and genotype analyses.

73W. Knibb et al. / Aquaculture 428–429 (2014) 71–78

2.6. Mitochondrial DNA sequencing and analysis

The control region was amplified and sequenced using the primers:5′-TCCTCTTGTTTTCCCCCTTT-3′ and 5′-GGATTCAATATAGGCATTTAT-3′designed from the completemitochondrial genome sequence of Penaeusmonodon (Wilson et al., 2000) (GenBank Accession No. NC002184).Reaction volumes of 25 μL contained approximately 10 ng of genomicDNA, 1× reaction buffer (67 mM Tris–HCl (pH 8.8), 16.6 mM (NH4)2SO4, 0.45% Triton X-100, 0.2 mg/mL gelatin), 200 μM of each dNTP,

1.5 mM MgCl2, 0.4 μM of each primer, and 0.25 U of Taq F1 DNApolymerase (Fisher Biotec; Wembley, Australia). PCR was performedusing a MaxyGene® Gradient Thermal Cycler (Axygen Scientific)with the following cycling conditions: initial denaturation step at 95 °Cfor 3 min; 35 cycles at 94 °C for 30 s, annealing temperature of 55 °Cfor 30 s, and 72 °C for 45 s; final extension at 72 °C for 10min. A sampleof each PCR product was run on 1.5% agarose 0.6xTBE gels to check bothamplification quality and quantity with subsequent generation of singlestranded forward sequences using DLoopF (9.6 pmol/reaction) as the

74 W. Knibb et al. / Aquaculture 428–429 (2014) 71–78

sequencing primer on an ABI 3750XL Genetic Analyser (AppliedBiosystems). Sequence chromatographs were imported intoSEQUENCHER v4.9 (Gene Codes Corp.), aligned, checked for accuracy,and edited where necessary. Sequences were subsequently exportedto MEGA v4 (Kumar et al., 1994) to assess nucleotide composition andvariation before conversion of the final alignment to a Windows basedformat using GeneDoc (Nicholas et al., 1997).

2.7. Development of microsatellites

Approximately 5 μg of a pooledDNA sample from20 F.merguiensis in-dividuals was submitted to the Australian Genome Research Facility(AGRF; Brisbane, Australia; http://agrf.org.au/) and used to construct arandom library that was sequenced using GS-FLX Titanium chemistry(RocheApplied Science;Mannheim, Germany). Sequenceswere trimmedfor length and quality using CLC Genomics Workbench v6 software (CLCBio, Aarhus, Denmark).We obtained 65,129 readswith an average lengthof 367 bp and searched for microsatellite loci having a minimum of sixrepeats for di-nucleotides and four repeats for tri- and tetra-nucleotides,using the QDDv2b pipeline (Meglécz et al., 2010) and PRIMER 3 (Rozenand Skaletsky, 1999). Primers were designed for 84 microsatellitecontaining sequences with suitable flanking regions.

Microsatellite loci were amplified individually in 12.5 μL reactionscontaining approximately 20 ng of genomic DNA, 1× reaction buffer(67 mM Tris–HCl (pH 8.8), 16.6 mM (NH4)2SO4, 0.45% Triton X-100,0.2 mg/mL gelatin), 200 μM of each dNTP, 2.0 mM MgCl2, 0.4 μM ofeach primer, and 0.5 U of Taq F1 DNA polymerase (Fisher Biotec;Wembley, Australia). PCR was performed using a MaxyGene®thermocycler (Axygen; Tewksbury, USA)with the following cycling con-ditions: initial denaturation step at 95 °C for 3min; 35 cycles at 94 °C for30 s, 52 °C for 30 s, and 72 °C for 45 s; with a final extension at 72 °C for10 min. PCR products were visualised on 3.0% agarose 0.6xTBE gels(140 V; ~110 min) stained with EtBr to look for evidence of polymor-phism, prior to labelling the forward primer of each of 16 potentiallyuseful microsatellite loci with FAM, VIC, NED or PET fluorescent dyes(Applied Biosystems; Foster City, USA).

Five lociwith consistent PCR amplification, clear allelic variation, andclarity of electrophoretic signatures were selected for genotyping. Oncevalidated in simplex, and prior tomultiplexing, primers were examinedfor possible interactions with a complementary threshold (the maxi-mum number of AT or CG matches for any two primers within a multi-plex reaction) set to seven, usingMultiplexManager software (Holleleyand Geerts, 2009). Two 5-plex reactions were then carried out usingQiagen Multiplex PCR Plus Kits (Qiagen, Germany) following themanufacturer's instructions. Final volumes were optimised (10 μL) aswell as the final concentration of Mastermix (0.6×), to reduce costs.PCR reactions contained 3.5 μL of sterile water, 3 μL of Qiagen MultiplexBuffer (2×), 1 μL of primer premix and 2.5 μL of DNA (10 ng/μL). Cyclingconditions were: an initial denaturation at 95 °C for 5 min, followed by35 cycles of 94 °C for 30 s, 57 °C for 90 s, and 72 °C for 30 s; with afinal extension at 68 °C for 10 min.

2.8. Genotyping and analysis

PCR products were separated by capillary electrophoresis on an AB3500 Genetic Analyser (Applied Biosystems). Fragment sizes weredetermined relative to an internal lane standard (GS-600 LIZ; AppliedBiosystems) using GENEMARKER v1.95 software (SoftGenetics; StateCollege, USA) and double-checked manually. Individuals with low ormissing peaks were amplified and genotyped a second time. MICRO-CHECKER v2.2.3 (van Oosterhout et al., 2004) was used to look forevidence of large allele dropout, extreme stuttering and null alleles,based on 1000 bootstraps and a 95% confidence interval. Tests forHWE at each locus and genotypic linkage equilibrium among pairs ofloci were conducted in FSTAT v2.9.3.2 (Goudet, 2001). Numbers ofalleles, observed and expected heterozygosities, and the fixation index

(FIS) as a measure of past inbreeding (Wright, 1965) were determinedusing GENALEX v6.41 (Peakall and Smouse, 2006). Polymorphic infor-mation content (PIC) was computed in CERVUS v3.0 (Kalinowski et al.,2007).

2.9. Statistical methods

2.9.1. Effective population sizeThe effective population size (Ne) for each lineage was estimated

based on linkage disequilibrium among the lineage's offspring usingthe programme NEESTIMATOR (Peel et al., 2004). The level of inbreed-ing at generation t was calculated following Falconer and MacKay(1996) as

Ft ¼ 1– 1–ΔFð Þt

where ΔF is the rate of inbreeding and ΔF = 12Ne

.Under the assumption of neutrality, there should be no correlation

between allele frequency and effect; the additive genetic variance atgeneration t in lineages was calculated as

VAt ¼ VA0

1− 1

2Ne

� �t

where VA0 is the additive genetic variance in the wild stock (basepopulation G0 and VA0 ~1).

Effective population size and inbreeding were also estimated usingthe COLONY software COLONY software (Jones and Wang, 2010).

2.9.2. Other testsMultivariate analysis were conducted using SPSS.

3. Results

3.1. MtDNA haplotype diversity among lines

To date, analysis of more than 50 wild F. merguiensis revealed thateach animal had a unique mtDNA sequence (Supplementary Table 4),so it is probable that there were about 20 haplotypes in the originalfirst founder collection of 20 wild caught animals and 20 haplotypes inthe second lot of introductions from thewild. Analysis of other indepen-dent wild samples revealed the same pattern (Knibb, unpublished).Accordingly we have assigned a value of 20 haplotypes at G0, for eachof the introductions, although this is an assumption.

Continuing with this assumption of unique mtDNA haplotypes forthe wild individuals, it would appear that a. the loss of mtDNA haplo-types was relatively rapid, over just a few generations, b. there wasloss for each line and c. for the long term lines, the loss continued pastgeneration 7 to generation 12 (Fig. 2). In the B × G crossed animals,records indicate the cross was G ♂ with B ♀ and no G haplotypeswere evident after the cross, but those from B were, so the crossingrecords match the observed mtDNA types. Similarly, for the G × Hcross, the records indicate that the cross was G ♂ with H ♀ whichagain matches the carried through mtDNA haplotypes. Accordingly,crossing should not have influenced the loss of mtDNA haplotypesover generations in the B and H lineages.

Considering all the data, the correlation between the number ofhaplotypes and generation was moderate and significant (r15 = − .589,P b 0.05).

3.2. Microsatellite allele diversity among lines

The original wild 20 female progenitors of lines A to G representabout 40 individuals, if we assume each female carried the spermato-phore of a wild male. Analysis of 40 different wild animals recentlycollected showed an average of 15.00 alleles over the 5 loci screened

0

5

10

15

20

25

0 1 2 3 4 5 6 7 8 9 10 11 12

To

tal n

um

ber

of

hap

loty

pes

i

n t

he

sam

ple

Generations

ABDEFGHI

Fig. 2. Number of mtDNA haplotypes over 14 generations and lines A, B, E, G, H and I. Note that line B females were crossed with line G males at generation 10 (with the mixed linecontinuing as line B) and H females were crossed with G males at generation 3 (with the mixed line continuing as line H).

75W. Knibb et al. / Aquaculture 428–429 (2014) 71–78

(viz adding all the alleles found for the five loci and dividing by 5). Weassume here that the original and more recently sampled wild animalsare representative of each other, an assumption supported by the totalcounts of different alleles that remained for the combined lines (seeSection 3.4). To permit comparison between lines, we randomly selectedjust 24 samples per line, which was the smallest sample size for anygiven line; these data are presented in Fig. 3. Over generations, theaverage number of alleles trended downward in all lines, even in theoutcrossed lines (B, G). Considering all the data of Fig. 3, the correlationbetween the number of different alleles and generation was moderateand significant (r16 = − .579, P b 0.05). The rate of loss appears to beless than that for the mtDNA: whereas an average of 75% or more ofthe haplotype diversity appears to be lost for a given line, only about50% of the allelic diversity appears to be lost for a given line.

3.3. Genetic variation among lines

The different lineages tended to have different mtDNA haplotypesand microsatellite alleles (see Supplementary Tables 1–4). For themicrosatellite data, pairwise FST values for the possible 120 comparisons(of 16 samples presented in Fig. 3) showed that only four were notstatistically significantly different from each other.

Over the five loci and a sample size of 24, there were a total of 66microsatellite alleles evident for the wild sample (75 were evidentusing a sample size of 40 wild animals). Of these 66 alleles, 57 were

0

2

4

6

8

10

12

14

0 1 2 3 4 5 6 7

Ave

rag

e n

um

ber

of

alle

les

ove

r 5

loci

Generatio

Fig. 3.Average number ofmicrosatellite alleles overfive loci (viz adding all the alleles found forline B females were crossed with line G males at generation 10 (with the mixed line continuingcontinuing as line H).

present in the six captive lines considered above (Fig. 3). Results whenconsidering 40 wild samples and further captive samples suggest thatwhilst variation has been lost within lines, the alleles have been main-tained, for the most part, among lines.

3.4. Effective population size

The effective population size (Ne) calculated based on linkage dis-equilibrium declined drastically with generations of selection (Table 1).The average Ne estimated from generation nine onwards was aboutnine compared with 263 in the founder (wild) stock. Ne estimatedfrom COLONY showed less dramatic declines.

From the reduction in Ne (from NEESTIMATOR) we could directlycalculate the accumulation of inbreeding which ranged from 11 to 76%cumulative across generations and across lineages. From the Ne wecould also calculate that there was a dramatic depletion of additivegenetic variance in this population of F. merguiensis. Note:Ne, inbreedingand additive genetic variance measures calculated using NEESTIMATORare not independent of each other.

4. Discussion

During the programme of mass selection over 14 generations, therewas an appreciation of the need to avoid loss of genetic variation andinbreeding, and care was taken to use a moderate number, up to 1000,

8 9 10 11 12 13 14

ns

ABEGHI

the five loci and dividing by 5) and 12 generations for lines A, B, D, E, F, G, H, and I. Note thatas line B) and H females were crossed with G males at generation 3 (with the mixed line

Table 1Effective population size (Ne from NEESTIMATOR), rate of inbreeding (ΔF, calculated from Ne from NEESTIMATOR), accumulated inbreeding level (F, calculated from ΔF and generationnumber), additive genetic variance (σ2

A), Ne estimated by COLONY (full likelihoodmethod assuming randommating and allowing inbreeding) and the average inbreeding coefficient es-timated by COLONY in different lines of the banana shrimp population at Seafarm.

Lineage Cohort name Generationa Ne from NEESTIMATOR ΔF F σ2A Ne from COLONY Inbreeding estimated from COLONY

A Wild 0 262.5 0.0019 0.0000 0.9981 69 0.0680A 7 13.3 0.0376 0.2353 0.7647 46 0.2260A 12 11.6 0.0431 0.4106 0.5894 23 0.0889A 13 4.8 0.1042 0.7607 0.2393 19 0.0000

B Wild 0 262.5 0.0019 0.0000 0.9981B 7 39.7 0.0126 0.0849 0.9151 37 0.0349B 11 61.4 0.0081 0.0860 0.9140 35 0.1522B 12 7.9 0.0633 0.5437 0.4871 24 0.0000

E Wild 0 262.5 0.0019 0.0000 0.9981 69 0.0680E 7 14.7 0.0340 0.2151 0.7849 32 0.0839n.a. n.a. n.a.E 14 24.7 0.0202 0.2490 0.7510 31 0.0711

G Wild 0 262.5 0.0019 0.0000 0.9981 69 0.0680G 7 12.2 0.0410 0.2539 0.7461 31 0.1097G 9 37.8 0.0132 0.1129 0.8871 42 0.0313G 11 13.9 0.0360 0.3317 0.6683 28 0.0534

H Wild 0 262.5 0.0019 0.0000 0.9981 69 0.0680H 3 20.8 0.0240 0.0704 0.9296 35 0.0355H 4 41.7 0.0120 0.0471 0.9529 35 0.0498

I Wild 0 262.5 0.0019 0.0000 0.9981 69 0.0680I 4 21.8 0.0229 0.0672 0.9328 15 0.0233I 5 34.3 0.0146 0.0570 0.9430 25 0.0157

n.a. = not available.a Generation derived from the wild.

76 W. Knibb et al. / Aquaculture 428–429 (2014) 71–78

of fertilised females per generation. Nevertheless, there were still sharpdeclines in the frequencies of mtDNA haplotypes and DNA microsatel-lite alleles over generations and over the different lines. The trajectoriesof the losses suggest that the individual lines were on track to becomemonomorphic with respect to this genetic variation in the next severalgenerations.

The rate of loss seemed higher for the mtDNA haplotypes than forthemicrosatellite alleles. This could be due simply to stochastic process-es, such as smaller effective sample sizes and greater genetic drift for themtDNA haplotypes: each broodstock damand sire pair would have onlyone possible mtDNA haplotype but 4 possible alleles for each of the fivemicrosatellite loci. So the effective sample size for the microsatellitealleles would be considerably greater than that for the DNA haplotypesand the sampling error (and chance for loss) would be less for themicrosatellite alleles. Also, since there was a high diversity of mtDNAin the wild samples, with each animal having a unique haplotype, it islikely that the frequencies of given mtDNA haplotypes in the founderswas very low (i.e. 5%), which would favour loss of mtDNA haplotypesover the more common microsatellite alleles. As well, and distinctfrom the rate of loss, once lines approach fixation (as some did), theycan be monomorphic for only one mtDNA haplotype but 5 microsatel-lite alleles, because there are five microsatellite loci.

Our results based on linkage disequilibrium showed that therewas adrastic decline in the effective population size (Ne) which correspondedto a very high level of inbreeding and consequently loss of geneticvariation as the domestication and selection processes progressed inthis F. merguiensis population. The low Nemay have resulted from inad-vertent selection of progeny from few parents producing the bestoffspring in subsequent generations or due to differential reproductiveand survival rates of brood stocks. The results obtained in our study(Ne = 5–25 in the latest generation) is generally consistent with thosereported in mass selection programmes for other farmed aquaculturespecies such as Gilthead seabream Sparus aurata with an Ne estimatedfrom 14 to 18 (Brown et al., 2005), in Barramundi Lates calcarifer withan Ne of 10–17 (Kumar et al., 1994) and in Pacific oysters Crassostreagigaswith an Ne of 9–36 (Appleyard andWard, 2006). One exceptional-ly high Ne (44–90) was reported in White shrimp Penaeus vannameifrom a mass selection over 12 generations by De Donato et al. (2005).Our results for population size together with those reported in other

species was significantly lower than that indicated as a minimum(i.e. 50–100) to constrain inbreeding at approximately 1% per genera-tion in selection programmes (Bijma et al., 2000; Ponzoni et al., 2010).

The dramatic decline in Ne over generations also resulted in signifi-cant inbreeding, based on linkage disequilibrium, in all lineages of thisbanana shrimp population (the accumulated inbreeding ranged from11 to 76% in the latest generations). High levels of inbreeding havealso been reported in other mass selection programmes, varying from3 to 5% per generation, depending on species, history of the population,duration of selection as well as genetic management practices (Blonket al., 2009; Brown et al., 2005). Interestingly, the levels of inbreedingpredicted fromCOLONYat each generationweremoremodest. Recentlyboth Duong et al. (2013) and Johnstone et al. (2013) found that the Ne

from COLONY was lower than Ne estimated by LDNE or NEESTIMATOR.One consequence of inbreeding is the reduction in additive genetic

variance in selected vs. wild stocks. Depletion of genetic variance maylimit selection response for banana shrimp. Indeed, rapid declines inselection response after four or five generations of mass selection wasreported in several aquatic animal species including as Silver barbPuntius gonionotus in Thailand (Pongthana et al., 2006). The fast accu-mulation of inbreeding and fast exhaustion of genetic variance partiallyexplainwhy anumber of earlier studies failed to achieve gains inmass se-lection programmes for tilapia (Hulata et al., 1986; Teichert-Coddingtonand Smitherman, 1988) or common carp, Cyprinus carpio (Moav andWohlfarth, 1976). However the lack of response to selection could bedue to other factors such as low levels of genetic variation in the basepopulation or genetic drift.

Overall, the great majority of the DNAmicrosatellite alleles detectedin the wild samples were also present when considering all the captivelines combined, indicating that the genotyped wild sample came fromthe same “stock” as the original progenitors. These data also indicatethat a. there was not a major bottleneck at the time of founding thestocks and b. much of the wild genetic variation, in terms of alleles,has persisted among if not within lines. Moreover, this phenomenonofmuchbetween line variation offers a future opportunity to rebuild ge-netic diversity through crossing among lineswithout resorting to the re-introduction of wild samples.

Whether or not maintaining multiple separate lines vs one singlelarge population is more effective in maintaining variation depends on

77W. Knibb et al. / Aquaculture 428–429 (2014) 71–78

several factors, including whether selection is done, how long (numberof generations) the lines are held, and so on (see, e.g. Lacy, 1987). Understochastic models, prior to fixation of alleles, and without selection, theaverage drift and loss of variation should be the same in one large pop-ulation vs that population split into multiple lines. Indeed, the inbreed-ing rate in the single large population would be less than in a givensubdivided line, and subdivision would thus risk greater inbreedingdepression, at least in the early generations. However, once fixationoccurs, as happened for some mtDNA haplotypes in the present study,the multiple lines will eventually keep more variation since differentlines will be fixed for different alleles in proportion to the starting allelefrequencies, whereas the single large population eventually will befixed for just one allele and one mtDNA haplotype (the latter as foundin some very long term P. monodon lines by Knibb, unpublished).

There are non-stochastic and other processes that accelerate fixationof alleles. First, there tend to be major unequal contributions of familiesto the next generation (Knibb et al., 2014). Second, intense selection offamilies can further contribute to unequal family contributions acrossgenerations (Knibb, 2000) so that even with large apparent populationsizes, as here, the effective population size may be quite small. Sinceshrimp are highly fecund, one or few outstanding families, within reason,can possibly dominate the next generation irrespective of whether largeor small populations are used. In which case, subdividing lines meansthat at least different single (or few) families will dominate in the differ-ent lines vs just one or few families in the single large population.

So, under some circumstances, we propose that the process ofreforming a diverse population, splitting it into a large number of linesfor various generations, may extend considerably the life of mass selec-tion programmes when compared to having continuously one singlelarge population. Indeed, in the simulations of Lacy (1987), “subdividedpopulations rapidly lose variability from within each subpopulation butretain variation across the subpopulations better than does a panmicticpopulation”. Lacy also concludes that “subpopulations could be mergedto produce a panmictic population that almost alwayswould bemore di-verse genetically than it would have been had it never been subdivided”.

Extending the longevity ofmass selection programmes can delay theneed to reintroduce new wild families which risks loss of genetic gain(as the wilds are unimproved) and also risks the introduction ofpathogens from the wild. A modified mass selection scheme was alsosuggested by Bentsen and Olesen (2002): whilst the scheme does notrequire individual identification, it still entails the conduct of pairmatings, initial maintenance of the progeny of such pair matings in aseparate enclosure, and controlled contribution of each full sib familyto the next generation at the time the animals are assigned to commu-nal rearing. Under these arrangements Bentsen and Olesen (2002)showed that inbreeding rates can be kept as low as 1% per generationif a minimum of 50 pairs are mated and the number of progeny testedfrom each pair is standardised to 30–50 progeny. Even so, when theidentity of parents is not known, it makes it difficult to restrict inbreed-ing effectively; hence, response to selection declined after a few gener-ations of selection using the Bentsen and Olesen model, as observed inSilver barb (Pongthana et al., 2006).

In summary, our results demonstrated that despite efforts to have alarge number of breeders in each generation, control of inbreeding andmaintenance of genetic variation are difficult to achieve in mass selec-tion programmes under commercial operations. We also found, due tothe diversity between lines, that some options exist to rebuild geneticvariation, and so for those mass selection programmes that cannotmaintain pedigrees, the use of multiple lines under some circumstancesmay be a hedge against loss of diversity. However, wherever it is possi-ble to maintain pedigrees, then the accumulation of inbreeding is prob-ably better managed with controlled pair matings. With full pedigreeinformation, inbreeding can be managed more effectively, matings ofclosely related individuals can be avoided, and advanced statisticaland mating methods such as optimal genetic contribution and factorialmating designs can be used.

Acknowledgements

We gratefully acknowledge the support of the Australian SeafoodCooperative Research Centre (Project No. 2009/724), the AustralianFisheries Research and Development Corporation, the AustralianPrawn Farmers Association, Seafarm at Cardwell and the University ofthe Sunshine Coast. We would also like to thank Roger Doyle for impor-tant contributions in particular regarding the matter of divided popula-tions and genetic variation.

Appendix A. Supplementary data

Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.aquaculture.2014.02.026.

References

Appleyard, S.A., Ward, R.D., 2006. Genetic diversity and effective population size in massselection lines of Pacific oyster (Crassostrea gigas). Aquaculture 254, 148–159.

Australian Bureau of Meteorology, 2012. http://www.bom.gov.au/ Accessed date 15November 2013.

Bentsen, H.B., Olesen, I., 2002. Designing aquaculture mass selection programs to avoidhigh inbreeding rates. Aquaculture 204.

Bijma, P., Van Arendonk, J.A.M., Woolliams, J.A., 2000. A general procedure for predictingrates of inbreeding in populations undergoingmass selection. Genetics 154, 1865–1877.

Blonk, R.J.W., Komen, J., Kamstra, A., Crooijmans, R., van Arendonk, J.A.M., 2009. Levels ofinbreeding in group mating captive broodstock populations of Common sole (Soleasolea) inferred from parental relatedness and contribution. Aquaculture 289, 26–31.

Brown, R.C., Woolliams, J.A., McAndrew, B.J., 2005. Factors influencing effective popula-tion size in commercial populations of gilthead seabream, Sparus aurata. Aquaculture247, 219–225.

De Donato, M., Manrique, R., Ramirez, R., Mayer, L., Howell, C., 2005. Mass selectionand inbreeding effects on a cultivated strain of Penaeus (Litopenaeus vannamei) inVenezuela. Aquaculture 247, 159–167.

Duong, T.Y., Scribner, K.T., Forsythe, P.S., Crossman, J.A., Baker, E.A., 2013. Interannualvariation in effective number of breeders and estimation of effective populationsize in long-lived iteroparous lake sturgeon (Acipenser fulvescens). Mol. Ecol. 22,1282–1294.

Evans, F., Matson, S., Brake, J., Langdon, C., 2004. The effects of inbreeding on performancetraits of adult Pacific oysters (Crassostrea gigas). Aquaculture 230, 89–98.

Falconer, D.S., Mackay, T.F.C., 1996. Introduction to Quantitative Genetics, Fourth ed.Longman, Essex CM20 2JE, England.

Goudet, J., 2001. FSTAT, a Program to Estimate and Test Gene Diversities and FixationIndices (Version 2.9. 3).

Holleley, C.E., Geerts, P.G., 2009. Multiplex Manager 1.0: a cross-platform computerprogram that plans and optimizes multiplex PCR. Biotechniques 46, 511.

Hulata, G., Wohlfarth, G.W., Halevy, A., 1986. Mass selection for growth rate in the Niletilapia (Oreochromis niloticus). Aquaculture 57, 177–184.

Hung, D., Vu, N.T., Nguyen, N.H., Ponzoni, R.W., Hurwood, D.A., Mather, P.B., 2013. Geneticresponse to combined family selection for improved mean harvest weight in giantfreshwater prawn (Macrobrachium rosenbergii) in Vietnam. Aquaculture 412–413,70–73.

Johnstone, D.L., O'Connell, M.F., Palstra, F.P., Ruzzante, D.E., 2013. Mature male parrcontribution to the effective size of an anadromous Atlantic salmon (Salmo salar)population over 30 years. Mol. Ecol. 22, 2394–2407.

Jones, O.R., Wang, J., 2010. Molecular marker-based pedigrees for animal conservationbiologists. Anim. Conserv. 13, 26–34.

Kalinowski, S.T., Taper, M.L., Marshall, T.C., 2007. Revising how the computer programCERVUS accommodates genotyping error increases success in paternity assignment.Mol. Ecol. 16, 1099–1106.

Kincaid, H.L., 1983. Inbreeding in fish populations used for aquaculture. Aquaculture 33,215–227.

Kinghorn, B.P., 2011. An algorithm for efficient constrained mate selection. Genet. Sel.Evol. 43, 4.

Knibb, W., 2000. Genetic improvement of marine fish—which method for industry?Aquac. Res. 31, 11–23.

Knibb, W., Quinn, J., Lamont, R., Whatmore, P., Nguyen, N.H., Remilton, C., 2014. Reproduc-tive behaviour of captive Fenneropenaeus merguiensis: evidence for monogamy andhigh between family variances for offspring number. Aquaculture 426–427, 60–65.

Kumar, S., Tamura, K., Nei, M., 1994. MEGA: molecular evolutionary genetics analysissoftware for microcomputers. Comput. Appl. Biosci. 10, 189–191.

Lacy, R.C., 1987. Loss of genetic diversity frommanaged populations: interacting effects ofdrift, mutation, immigration, selection, and population subdivision. Conserv. Biol. 1,143–158.

Loughnan, S.R., Domingos, J.A., Smith-Keune, C., Forrester, J.P., Jerry, D.R., Beheregaray,L.B., Robinson, N.A., 2013. Broodstock contribution after mass spawning and sizegrading in barramundi (Lates calcarifer, Bloch). Aquaculture 404–405, 139–149.

Loukovitis, D., Sarropoulou, E., Vogiatzi, E., Tsigenopoulos, C.S., Kotoulas, G., Magoulas, A.,Chatziplis, D., 2012. Genetic variation in farmed populations of the gilthead seabream (Sparus aurata) in Greece using microsatellite DNA markers. Aquac. Res. 43,239–246.

78 W. Knibb et al. / Aquaculture 428–429 (2014) 71–78

Meglécz, E., Costedoat, C., Dubut, V., Gilles, A., Malausa, T., Pech, N., Martin, J.-F., 2010.QDD: a user-friendly program to select microsatellite markers and design primersfrom large sequencing projects. Bioinformatics 26, 403–404.

Moav, R., Wohlfarth, G., 1976. Two-way selection for growth rate in the common carp(Cyprinus carpio L.). Genetics 82, 83–101.

Nguyen, N.H., Ponzoni, R.W., Abu-Bakar, K.R., Hamzah, A., Khaw, H.L., Yee, H.Y., 2010.Correlated response in fillet weight and yield to selection for increased harvestweight in genetically improved farmed tilapia (GIFT strain), Oreochromis niloticus.Aquaculture 305, 1–5.

Nicholas, K.B., Nicholas, H., Deerfield, D., 1997. GeneDoc: analysis and visualization ofgenetic variation. EMBnet News 4, 14.

Peakall, R., Smouse, P.E., 2006. GENALEX 6: genetic analysis in Excel. Population geneticsoftware for teaching and research. Mol. Ecol. Notes 6, 288–295.

Peel, D., Ovenden, J.R., Peel, S.L., 2004. NEESTIMATOR: Software for Estimating EffectivePopulation Size. Queensland Government, Department of Primary Industries andFisheries, Brisbane, Australia.

Pongthana, N., Nguyen, N.H., Ponzoni, R.W., 2006. Mass selection for increased bodyweight at harvest over five generations in Thai Silver barb (Puntius gonionotus).Proceedings of the 8th World Congress on Genetics Applied to Livestock Production,Belo Horizonte, Minas Gerais, Brazil, 13–18 August, 2006. paper #7 in section 09.

Ponzoni, R.W., Khaw, H.L., Nguyen, N.H., Hamzah, A., 2010. Inbreeding and effectivepopulation size in theMalaysian nucleus of the GIFT strain of Nile tilapia (Oreochromisniloticus). Aquaculture 302, 42–48.

Rozen, S., Skaletsky, H., 1999. Primer3 on the WWW for general users andfor biologist programmers. In: Krawetz, S., Misener, S. (Eds.), Bioinformatics

Methods and Protocols: Methods in Molecular Biology. Springer, HumanaPress, Totowa, NJ, pp. 365–386.

Teichert-Coddington, D.R., Smitherman, R.O., 1988. Lack of response by Tilapianilotica to mass selection for rapid early growth. Trans. Am. Fish. Soc. 117,297–300.

van Oosterhout, C., Hutchinson, W.F., Wills, D.P., Shipley, P., 2004. Micro‐checker:software for identifying and correcting genotyping errors in microsatellite data.Mol. Ecol. Notes 4, 535–538.

Vela Avitúa, S., Montaldo, H.H., Márquez Valdelamar, L., Campos Montes, G.R., CastilloJuárez, H., 2013. Decline of genetic variability in a captive population of Pacificwhite shrimp Penaeus (Litopenaeus vannamei) using microsatellite and pedigreeinformation. Electronic Journal of Biotechnology 16 (4). http://dx.doi.org/10.2225/vol16-issue4-fulltext-11.

Wang, S., Hard, J., Utter, F., 2001. Salmonid inbreeding: a review. Rev. Fish Biol. Fish. 11,301–319.

Wilson, K., Cahill, V., Ballment, E., Benzie, J., 2000. The complete sequence of themitochondrial genome of the crustacean Penaeus monodon: are malacostracancrustaceans more closely related to insects than to branchiopods? Mol. Biol. Evol.17, 863–874.

Wright, S., 1965. The interpretation of population structure by F-statistics with specialregard to systems of mating. Evolution 395–420.