Research ArticleUnraveling the Limits of Mitochondrial Control Region toEstimate the Fine Scale Population Genetic Differentiation inAnadromous Fish Tenualosa ilisha

Rashmi Verma1 Mahender Singh1 and Sudhir Kumar2

1National Bureau of Fish Genetic Resources Lucknow Uttar Pradesh 226 002 India2University of Lucknow Lucknow Uttar Pradesh 226 007 India

Correspondence should be addressed to Mahender Singh drmahendersingh29gmailcom

Received 17 December 2015 Accepted 12 April 2016

Academic Editor Muzaffar Hussain

Copyright copy 2016 Rashmi Verma et alThis is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

The mitochondrial control region has been the first choice for examining the population structure but hypervariability andhomoplasy have reduced its suitability We analysed eight populations using control region for examining the population structureof Hilsa Although the control region analysis revealed broad structuring between the Arabian Sea and Bay of Bengal (119865ST 00441119901 lt 0001) it was unable to detect structure among riverine populations These results suggest that the markers used must be ableto distinguish populations and control region has led to an underestimation of genetic differentiation among populations ofHilsa

1 Introduction

The Hilsa shad Tenualosa ilisha is an anadromous fish withbroad distribution ranging from foreshore areas estuariesbrackish water lakes and freshwater rivers Indo-West Pacificregion from the Persian Gulf along the coast of PakistanIndia Bangladesh and Burma to South Vietnam [1] Itascends the rivers for breeding during the monsoon seasonand returns to the sea after completion of spawning Therehas been increased exploitation on Hilsa fisheries in east andwest coast of India over the years because of the introductionof efficientmechanized crafts such asmoshari (mosquito net)seine net behundi jal (set bag net) and char ghera jal (fence-like net operation around the char) and makeshift gears andfishers are tempted to exploit Hilsa stocks without caring forsize and season Overfishing may reduce population sizes toa level at which inbreeding and loss of genetic diversity occurand may result in extinction of local populations [2] Properscientific and judicious management actions by assessing thegenetic make-up and variability of fish stockmust be taken toensure sustainability of Hilsa population

The rate of evolution of mitochondrial DNA is generallyhigher than that of nuclear genes due to the lack of a known

repair mechanism for mutations that arise during replication[3] The control region (about 1 KB) has role in initiationof replication and transcription and is the marker of choiceto identify population connectivity conservation units andmigration routes It has been used in many phylogenetic andpopulation genetic studies due to high copy number andhigh mutation rate as well as its maternal and haploid modeof inheritance [4] However high evolutionary rate that hasmade the control region an attractive marker for biologistsmay be masking the true relationships between populationsdue to high haplotype diversity as well as homoplasy So analternative marker with a slower evolutionary rate may bemore suitable than the control region to reveal the populationstructure of Hilsa [5]

The population structuring of Hilsa was investigated byvarious researchers using morphomeristical biochemical andmolecular approaches and also they differentiated three stocksof Hilsa belonging to Hooghly Padma and Ganga rivers usingbiometrical parameters Based on tagging experiment Pillayet al [6] concluded that the same Hilsa individuals come upthe Hooghly River during subsequent seasons that is win-ter and monsoon Ghosh et al [7] differentiated Hilsa intoslender and broad morphotypes using morphometric data

Hindawi Publishing CorporationScientificaVolume 2016 Article ID 2035240 9 pageshttpdxdoiorg10115520162035240

2 Scientifica

Brahmane et al [8 9] reported more than one stock of Hilsafrom India using RAPD markers and cytochrome b regionSo the baseline information on genetic stocks needs to beauthenticated However several morphometric and molecu-lar studies (RAPD RFLP) were conducted in Bangladesh andIndia but no study was done using mitochondrial D-loop tounderstand population genetic structure and patterns of geneflow of T ilisha [10]

2 Materials and Methods

21 Fish Sampling The present study included 77 specimensof T ilisha from the geographical distribution range in Indianamely rivers draining inBay of Bengal (ie GangaHooghlyandGodavari) as well as fromDiamondHarbour and ParadipPort and rivers draining to Arabian Sea (ie Narmada andTapti) T ilisha was identified and discriminated from T toliand Hilsa kelee based on morphometric and meristic datafollowing Talwar and Jhingran [11] and Fisher andWhitehead[12] The Hilsa body is oblong and compressed with 30ndash33spines like scutes on abdomen Difference between twomajorHilsa species that is T ilisha and T toli is very minute Inthe former dorsal and ventral profile of the body is equallyconvex while in the later abdominal profile is more convexthan that of dorsal Further about 150 to 200 straight toslightly curved gill rakers are present on the lower part of firstarch in T ilisha while in T toli gill rakers are curved and thenumber of gill rakers is 80 to 90 as discussed by Huda andHaque [13] The fish specimens were photographed on graphpapers and meristic counts of the specimens were comparedMuscle and fin tissues were preserved in 95 vv ethanol andthe vouchers were kept in 10 vv formaldehyde Specific andunambiguous code was given to tissue samples and voucherof each fish specimen (Table 1)

22 DNA Extraction PCR Amplification and DNA Sequenc-ing Approximately 50mg of caudal or anal fin or mus-cle tissue was used for DNA isolation following standardphenol chloroform isoamyl alcohol method [14] Precipi-tated DNA was resuspended in TE buffer (10mM Tris-HCl01mM EDTA and pH 8) and concentration was determinedusing Nanodrop 2000 (Thermo Scientific USA)The primersTIDF (51015840-AACTTCCACCCCTAACTCCC-31015840) and TIDR(51015840-GTGCTTGCGGGGCTTG-31015840) were designed usingPrimer3 [15] and BLASTn [16] software of NCBI so as toamplify complete control region of mitochondrial DNA(mtDNA) The PCR reaction of 50 120583L volume contained 1xbuffer 100120583M dNTPs 2mM MgCl

2 10 picomoles of each

primer 3U TaqDNA polymerase and 100 ng template DNAAmplifications were performed in Veriti 96 fast thermalcycler (Applied Biosystems Inc USA) The thermal regimefor control region consisted of initial denaturation of 3minat 94∘C followed by 35 cycles of denaturation at 94∘C for50 sec annealing at 47∘C for 30 sec and extension at 72∘C for80 sec with final extension of 10min at 72∘C PCR productswere visualized on 1 agarose gels stained with ethidiumbromide and documented using a gel documentation system(UVP USA) DNA sequencing was performed followingthe dideoxynucleotide chain termination method [17] using

an automated ABI 3730 sequencer (Applied Biosystems IncUSA)

23 Sequence Analysis Complete control region sequencewas generated from forward and reverse sequence readsusing MEGA 51 [18] Ambiguities were referred againstthe sequencing electropherogramsThe consensus sequenceswere blasted in NCBI for the nearest similar sequencematches using BLASTn and submitted to GenBank (Table 1)DNA sequences were analysed by ClustalW Arlequin version35 [19] DnaSP version 510 [20] and MEGA 51 softwarefor nucleotide composition number of polymorphic sites (119878)haplotype diversity (ℎ) and nucleotide diversity (120587)

The evolutionary history was inferred using the MLmethod The bootstrap consensus tree inferred from 1000replicates is taken to represent the evolutionary history ofthe taxa analysedThe evolutionary distances were computedusing the Kimura 2-parameter method [21] and are in theunits of the number of base substitutions per site Therate variation among sites was modelled with a gammadistribution (shape parameter = 1)

24 Haplotype Analysis Minimum spanning network ofhaplotypes was prepared by Network 46 software [22]Intrapopulation diversity was analysed by estimating hap-lotype diversity which indicates the probability that tworandomly chosen haplotypes are different and nucleotidediversity which indicates the probability that two randomlychosen homologous nucleotides are different

25 Population Genetic Analysis The isolation-by-distanceeffects on population genetic structure were estimated byIBDWS 323 and pairwise 119865ST statistics [23] using Arlequin35 The hierarchical nesting of genetic diversity was esti-mated using the analysis of molecular variance (AMOVA)approach and was calculated using Arlequin 35 Significanceof pairwise population comparisons was tested by 20000permutations The AMOVA tests were organized in a hierar-chical manner and 1000 permutation procedures were usedto construct null distributions and to test the significance ofvariance components [24] To detect population expansionor contraction Tajimarsquos D and Fursquos FS values were estimatedbased on pairwise differences between sequences TajimarsquosD test [25] calculates the distribution of allele frequency ofsegregating sites whereas Fursquos FS test [26] is based on thedistribution of alleles or haplotypes

3 Results

31 Molecular Characterization and Genetic Diversity Table 1shows the number of samples (119873) number of haplotypes(119873Hap) haplotype diversity (Hap119863) and nucleotide diver-sity (120587) for each population A total of 77 individuals weresequenced for the mtDNA control region (873 bp)

Among 77 samples 94 polymorphic sites and 58 hap-lotypes were detected These polymorphisms included 64parsimony informative sites and 32 singleton sites Thenucleotide composition () was 317 (A) 279 (T) 237 (C)and 167 (G)

Scientifica 3

Table1Detailoffi

shsamplingshaplotype

diversitynucleotided

iversityandGenBa

nkaccessionnu

mbersin

Tilisha

Popu

latio

nsSamples

ize

(119873)

Num

bero

fhaplotypes

(119873Hap)

Latitud

elong

itude

GenBa

nkaccessionnu

mbers

Haplotype

diversity

(Hap119863)

Nucleotided

iversity

(120587)

Hoo

ghlyFeeder

CanalFarakk

awestB

engal

88

2448N

8755E

KU214744

-KU214751

1000plusmn000391

001480plusmn000

00027

Diamon

dHarbo

urw

estB

engal

86

2447N

8755E

KU214760-K

U214766

0893plusmn001238

00100

9plusmn000

00030

Ganga

riverbelow

Farakk

aBarrage

Maldaw

estB

engal

77

2251N

8822E

KU214806-K

U214820

1000plusmn000583

001629plusmn000

00049

Narmadar

iverB

arkalGujarat

1312

2210

N8810E

KU214752-K

U214759

0987plusmn000125

001566plusmn000

0006

6Paradippo

rtO

disha

99

2019

N8636E

KU214780-K

U214788

1000plusmn000274

001204plusmn000

00062

God

avaririverR

ajahmun

dry

And

hraP

radesh

87

1656N

814

4EKU

214789-K

U214795

1000plusmn000583

001207plusmn000

0006

8

TaptiriverU

kaid

amSuratG

ujarat

85

2155N

7325E

KU214767-K

U214779

0756plusmn001678

001835plusmn000

00059

Hoo

ghly

KalyaniwestB

engal

1612

2115

N7335E

KU214796-K

U214805

0962plusmn000159

000

935plusmn000

00012

4 Scientifica

The haplotype diversity (ℎ) of the analysed populationswas rather high with observed values between 1000 plusmn 00058in Ganga and 0756 plusmn 001678 in Tapti population Thenucleotide diversity (120587) within each population was very lowranging from 000935 plusmn 00000012 in Hooghly to 001835 plusmn00000059 in Tapti population (Table 1)

32 Haplotype Distribution and Phylogenies In phylogeneticstudy (Figure 1) and minimum spanning network of 58 hap-lotypes (Figure 2) eight populations of T ilishawere groupedin four lineages The haplotypes shared among differentpopulations were 258 and the rest of 742 were privatehaplotypes Hap 8 was the most common haplotype sharedamong Hooghly Feeder Canal Paradip Port Hooghly andDiamond Harbour populations Hap 7 was shared amongParadip Port andHooghly Feeder Canal populations Hap 24andHap 27were shared between Tapti andNarmada popula-tions Hap 1 was shared between Hooghly Feeder Canal andGanga populations Hap 15 was shared betweenHooghly andGanga populations (Table 2)

33 Population Genetic Analysis There is significant geo-graphical structuring among populations only when all pop-ulations were grouped in Bay of Bengal and Arabian SeaAMOVA revealed 442 variation among populations and9558 variation within population (Table 3) which wasfurther supported by significant 119865ST value (ie 00441 119901 lt0001) Estimates of genetic differentiation between eightpopulations using 119865 statistics are given in Table 4 The popu-lations from Narmada and Tapti showed high level of geneticdifferentiation from other populations

Tajimarsquos D was nonsignificantly negative for all exceptNarmada and Tapti populations Fursquos FS test also showednonsignificant negative values for all except Diamond Har-bour and Tapti populations Large differences were observedbetween 120579

0(population before expansion) and 120579

1(population

after expansion) In addition the SSD and Hri values werealso nonsignificant except for Diamond Harbour and Taptipopulations (Table 5) Also low haplotype diversity andmoderate to high nucleotide diversity was observed in Dia-mondHarbour andTapti populationsMismatch distributioncurves were constructed to study the genetic bottleneck andall histograms presented multimodal curves characteristic ofpopulations with constant size over time The plot betweengenetic distance and geographic distances showed a highlysignificant positive correlation indicating that geographicdistance corroborates variation in genetic distance betweenHilsa populations (1199032 = 0230 119901 le 001) (Figure 3)

4 Discussion

The significant positive correlation between geographical andgenetic distances usually referred to as ldquoisolation by distancerdquois typically suggestive of migration-drift equilibrium Ourresult showed high haplotype diversity (high genetic varia-tion) and lownucleotide diversity and significant positive FursquosFS and TajimarsquosD test except for Tapti andDiamondHarbourindicative of recent population expansion after a genetic bot-tleneck or founder events [27 28]

Hap_6Hap_37Hap_17Hap_3Hap_18Hap_36Hap_55Hap_16Hap_35Hap_33Hap_51Hap_57Hap_10Hap_34Hap_53Hap_15Hap_45Hap_32Hap_7Hap_5Hap_54Hap_11Hap_58Hap_4Hap_9Hap_44Hap_49Hap_52Hap_13Hap_2Hap_19Hap_38Hap_50Hap_56Hap_12Hap_14Hap_41Hap_1Hap_42Hap_8Hap_43Hap_39Hap_40Hap_46Hap_24Hap_47Hap_26Hap_48Hap_20Hap_30Hap_28Hap_23Hap_22Hap_25Hap_31Hap_29Hap_21Hap_27

51

53

64

79

55

58

74

5564

5569

63

71

80

6153

5381

100

6789

100 Lineage 4

Lineage 1

Lineage 3

Lineage 2

Figure 1 Molecular phylogenetic analysis of 58 haplotypes of Tilisha constructed by Maximum Likelihood method

In this study 119865 statistics (119865ST) AMOVA haplotype net-work and phylogenetic analysis revealed genetic differentia-tion among the T ilisha populations suggesting that T ilishadoes not have a single panmictic population Brahmane etal [8] separated GangaYamuna rivers stock from HooghlyandNarmada using RAPDmarkers while Brahmane et al [9]reported low genetic diversity and absence of population dif-ferentiation of Hilsa by using cytochrome b region in Ganga

Scientifica 5

Table 2 Relative haplotype frequencies in different populations of T ilisha

Haplotype Hooghly Feeder Canal Diamond Harbour Ganga Narmada Paradip Port Godavari Tapti HooghlyHap 1 0125 0 0143 0 0 0 0 0Hap 2 0125 0 0 0 0 0 0 0Hap 3 0125 0 0 0 0 0 0 0Hap 4 0125 0 0 0 0 0 0 0Hap 5 0125 0 0 0 0 0 0 0Hap 6 0125 0 0 0 0 0 0 0Hap 7 0125 0 0 0 0111 0 0 0Hap 8 0125 0375 0 0 0111 0 0 0066Hap 9 0 0125 0 0 0 0 0 0Hap 10 0 0125 0 0 0 0 0 0Hap 11 0 0125 0 0 0 0 0 0Hap 12 0 0125 0 0 0 0 0 0Hap 13 0 0125 0 0 0 0 0 0Hap 14 0 0 0143 0 0 0 0 0Hap 15 0 0 0143 0 0 0 0 0133Hap 16 0 0 0143 0 0 0 0 0Hap 17 0 0 0143 0 0 0 0 0Hap 18 0 0 0143 0 0 0 0 0Hap 19 0 0 0143 0 0 0 0 0Hap 20 0 0 0 00769 0 0 0 0Hap 21 0 0 0 00769 0 0 0 0Hap 22 0 0 0 00769 0 0 0 0Hap 23 0 0 0 00769 0 0 0 0Hap 24 0 0 0 0154 0 0 01 0Hap 25 0 0 0 00769 0 0 0 0Hap 26 0 0 0 00769 0 0 0 0Hap 27 0 0 0 00769 0 0 05 0Hap 28 0 0 0 00769 0 0 0 0Hap 29 0 0 0 00769 0 0 0 0Hap 30 0 0 0 00769 0 0 0 0Hap 31 0 0 0 00769 0 0 0 0Hap 32 0 0 0 0 0111 0 0 0Hap 33 0 0 0 0 0111 0 0 0Hap 34 0 0 0 0 0111 0 0 0Hap 35 0 0 0 0 0111 0 0 0Hap 36 0 0 0 0 0111 0 0 0Hap 37 0 0 0 0 0111 0 0 0Hap 38 0 0 0 0 0111 0 0 0Hap 39 0 0 0 0 0 0143 0 0Hap 40 0 0 0 0 0 0143 0 0Hap 41 0 0 0 0 0 0143 0 0Hap 42 0 0 0 0 0 0143 0 0Hap 43 0 0 0 0 0 0143 0 0Hap 44 0 0 0 0 0 0143 0 0Hap 45 0 0 0 0 0 0143 0 0Hap 46 0 0 0 0 0 0 02 0Hap 47 0 0 0 0 0 0 01 0Hap 48 0 0 0 0 0 0 01 0Hap 49 0 0 0 0 0 0 0 0066Hap 50 0 0 0 0 0 0 0 0066Hap 51 0 0 0 0 0 0 0 0066Hap 52 0 0 0 0 0 0 0 0066Hap 53 0 0 0 0 0 0 0 0066Hap 54 0 0 0 0 0 0 0 02Hap 55 0 0 0 0 0 0 0 0066Hap 56 0 0 0 0 0 0 0 0066Hap 57 0 0 0 0 0 0 0 0066Hap 58 0 0 0 0 0 0 0 0066

6 Scientifica

H_1

H_2

H_3

H_4

H_5

H_6

H_7H_8

H_9

H_10

H_11

H_12

H_13

H_14

H_15

H_16

H_17

H_18H_19

H_20

H_21

H_22

H_23

H_24

H_25

H_26

H_27

H_28

H_29

H_30

H_31

H_32 H_33

H_34

H_35

H_36

H_37

H_38

H_39H_40H_41

H_42

H_43

H_44

H_45 H_46

H_47

H_48

H_49H_50

H_51

H_52

H_53

H_54

H_55 H_56

H_57

H_58

mv1

mv2mv3

mv4

mv5

mv6 mv7

mv8

mv9

mv10

mv11

mv12 mv13mv14

mv15

mv16mv17

mv18

mv19

mv20

mv21

mv22

mv23mv24

mv25

mv26

Lineage 2

Lineage 3

Lineage 1

Lineage 4

Figure 2 Minimum spanning network of T ilisha based on D-loop haplotypes Haplotypes separated by single lines are one mutation apartand small circles along lines represent missing haplotypes (not sampled or extinct)

minus200minus1000 1399 2199 2999599Geographic distance

minus010

minus007

minus004

minus001

002

005

008

011

014

017

020

Gen

etic

dist

ance

Figure 3 Plot of genetic distance versus geographical distance (inKms) for analysing isolation-by-distance patterns in T ilisha

and Hooghly rivers and also felt the need of advanced geneticmarkers like ATPase 86 nicotinamide dehydrogenase sub-unit 2 (ND2) andmicrosatellite for confirmation Our resultsalso strengthen the presence of more than one stock of Hilsain Indian subcontinent however Ganga and Hooghly FeederCanal and Hooghly populations shared the same genepool

Geographical divergence and gene flow among subpopula-tions can be assessed by phylogenetic analysis and haplotypenetwork provided that gene lineages have accumulated suffi-cient polymorphisms over time and populations have beenisolated completely to allow genetic drift to act In presentstudy ML tree and haplotype network of control region haveclearly distinguished four major cladeshaplogroups namelyLineage 1 (Bay of Bengal populations Diamond HarbourGanga Hooghly Feeder Canal Godavari Paradip Port andHooghly) Lineage 2 (Godavari + some haplotypes from Bayof Bengal population) Lineage 3 (Tapti population) and Lin-eage 4 (Narmada population) Lineage 2 had mixed haplo-types mostly from Godavari and some from Hooghly FeederCanal (Hap 1) DiamondHarbour (Hap 12) Ganga (Hap 14)and common haplotype (Hap 8) this can be explained byslightlymistakenmigration routes taken by some individualsHowever formation of four clades may be explained by thephilopatry driven genetic differentiation that also corrobo-rates previous tagging studies that demonstrated limited nataland breeding dispersal ofHilsa [6]This type of behaviour hasalso been reported in highly studied American shad whichexhibits a high rate of philopatry with 97 of spawnersreturning to their natal stream [29] These results were alsoconfirmed by hierarchical AMOVA analysis that is among-group analysis (two-gene pool analysis of Ganga HooghlyHooghly Feeder Canal Diamond Harbour Paradip PortGodavari and Tapti and Narmada gene pools) having signifi-cant variation of 361 This behavioural isolation of Hilsapopulations was also strongly supported by pairwise signifi-cant 119865ST values of Tapti and Narmada with Godavari ParadipPort Diamond Harbour Hooghly Feeder Canal and Ganga

Scientifica 7

Table 3 Hierarchal analysis of molecular variance (AMOVA) for T ilisha

Source of variation df Sum of squares Variance components Percentage of variationAmong populations 7 4773 002189119881

119886442

Within populations 69 32681 047364119881119887

9558Total 76 37455 049553Fixation index 119865ST 00441

Table 4 Pairwise 119865ST (below diagonal) and associated 119901 values (above diagonal) among T ilisha populations Bold values denote statisticalsignificant values after the Bonferroni correction

Hooghly Feeder Canal Diamond Harbour Ganga Narmada Paradip Port Godavari Tapti HooghlyHooghly Feeder Canal 0 05855 09909 048649 099099 099099 003604 022523Diamond Harbour 00070 0 016216 003604 028829 019820 000000 003604Ganga minus00181 00547 0 050450 099099 099099 003604 053153Narmada 00067 00570lowast 000687 0 044144 062162 003604 003604Paradip Port minus00285 00115 0 00066 0 099099 000000 031532Godavari 0 00547 0 00068 0 0 001802 018018Tapti 0127lowast 01788lowastlowast 01302lowast 00749lowast 01243lowastlowast 01302lowast 0 000000Hooghly 00120 00462lowast 00015 00256lowast 00126 00207 01351lowastlowast 0lowastlowast119901 lt 0001 lowast119901 lt 005

Table 5 Genetic diversity indices and demographic parameters of T ilisha

Population ti tv 1205790

1205791

Tajimarsquos119863 Fursquos 119865119878 Hri SSDHooghly 26 1 7490 99999 minus007622 minus27044 004227 001401Diamond Harbour 22 1 7200 99999 minus005012 078291 016709 010294lowast

Hooghly Feeder Canal 31 6 4826 99999 minus051513 minus16082 012245 004966Paradip Port 31 6 20202 99999 minus115597 minus26925 002623 002041Godavari 22 9 2638 99999 minus080192 minus14708 009524 005183Ganga 30 6 12179 99999 minus019653 minus09430 024943 007201Narmada 37 2 17159 201765 041084 minus23072 002548 002592Tapti 27 6 12237 99999 199567 555599 013975 011588lowastlowast119901 lt 005

Although phylogenetic and phylogeographic analysis (mini-mum spanning network) shows that genetic variation is notrandomly distributed among Bay of Bengal rivers a patternof population structure and gene flow has been difficultto verify statistically Analysis of pairwise population 119865STprovides strong support for divergence betweenBay of Bengaland Arabian Sea population however biologically acceptedlevel of statistical significance (119901 le 005) is too stringent toreveal the subtle genetic differences within Bay of Bengal andArabian Sea populations when the Bonferroni correction formultiple 119901 value is applied [30]

The genetic marker most commonly used to elucidatepopulation structure in fish has been control region andwas considered sensitive enough to test for structure amongmost populations due to rapid accumulation of mutationin the control region as well as the simplicity of mtDNAinheritance through maternal line [3] However its suitabil-ity is questionable at interbasin level as true relationship

between populations was masked by high haplotype diversityand homoplasy 119865 statistics (119865ST) and AMOVA could notclearly demarcate the population structure among popu-lations from Bay of Bengal Mazumder and Alam [10]used RFLP of mitochondrial D-loop region to differen-tiate riverine estuarine and marine stocks and observedsignificant differentiation between the riverine and marine(Coxrsquos Bazar) populations but not between the marine andone of the estuarine populations as electrophoretic analysisof PCR-RFLP has lower resolving power than sequencingof the same PCR product They suggested sequencing ofD-loop region and faster evolving molecular markers forpopulation structure studies such as microsatellite lociSimilar results were recorded for yellowfin tuna whereanalysis of the control region between Atlantic and Indo-Pacific populations was unable to detect structure but clearlyresolved by PCR RFLP of the ATPase 68 and COI III genes[31]

8 Scientifica

5 Conclusion

The populations are supposed to be separated by the philo-patric behaviour Phylogeographical structure defined byAMOVA and region specific haplotypes distinguished pop-ulations up to sea level (Bay of Bengal and Arabian sea) butpopulation structuring at basin level was not noticed usingD-loop as a marker so we suggest the use of somemoderatelyevolvingmarkers such as ATPase 86 nicotinamide dehydro-genase subunit 2 (ND2) and microsatellites for elucidatingpopulation structure of Hilsa

Disclosure

Theauthors declare that all the experiments performed in thisstudy are in compliancewith the current laws of their country

Competing Interests

The authors declare that there are no competing interestsregarding the publication of this paper

Acknowledgments

The authors are thankful to the Director of National Bureauof Fish Genetic Resources for support and guidance

References

[1] S R Pillay and H Rosa Jr ldquoSynopsis of biological data on hilsaHilsa ilisha (Hamilton 1882)rdquo FAO Fisheries Biology Synopsis25 Rome Italy 1963

[2] W S Lakra V Mohindra and K K Lal ldquoFish genetics andconservation research in India status and perspectivesrdquo FishPhysiology and Biochemistry vol 33 no 4 pp 475ndash487 2007

[3] J C Avise Molecular Markers Natural History and EvolutionChapman and Hall New York NY USA 1994

[4] J Avise J Arnold R Ball et al ldquoIntraspecific phylogeographythe mitochondrial DNA bridge between population geneticsand systematicsrdquo Annual Review of Ecology Evolution andSystematics vol 18 pp 489ndash522 1987

[5] H M Bradman P M Grewe and B Appleton ldquoDirect com-parison of mitochondrial markers for the analysis of swordfishpopulation structurerdquo Fisheries Research vol 109 no 1 pp 95ndash99 2011

[6] S R Pillay K V Rao and P K Mathur ldquoPreliminary report onthe tagging of the HilsaHilsa ilisha (Hamilton)rdquo Proceedings ofthe Indo-Pacific Fisheries Council India 1962

[7] A N Ghosh R K Bhattacharya and K V Rao ldquoOn the iden-tification of the sub-populations of Hilsa ilisha (Ham) in theGangetic system with a note on their distributionrdquo Proceedingsof the National Academy of Sciences India Section B BiologicalSciences vol 34 pp 44ndash57 1968

[8] M P Brahmane M K Das M R Sinha et al ldquoUse of RAPDfingerprinting for delineating populations of hilsa shad Tenu-alosa ilisha (Hamilton 1822)rdquo Genetics and Molecular Researchvol 5 no 4 pp 643ndash652 2006

[9] M P Brahmane S N Kundu M K Das and A P SharmaldquoLowgenetic diversity and absence of population differentiationof hilsa (Tenualosa ilisha) revealed bymitochondrial DNA cyto-chrome b region in Ganga and Hooghly riversrdquo African Journalof Biotechnology vol 12 no 22 pp 3383ndash3389 2013

[10] S K Mazumder and M S Alam ldquoHigh levels of genetic vari-ability and differentiation in hilsa shad Tenualosa ilisha (Clu-peidae Clupeiformes) populations revealed byPCR-RFLP anal-ysis of the mitochondrial DNA D-loop regionrdquo Genetics andMolecular Biology vol 32 no 1 pp 190ndash196 2009

[11] K Talwar and A G Jhingran Inland Fishes of India and Adja-cent Countries Balkema Rotterdam The Netherlands 1991

[12] W Fisher and P J P Whitehead FAO Species IdentificationSheets Fishing Area 57 and 71 FAO Rome Italy 1974

[13] S M Huda andM E Haque Field Guide to Finfishes of Sundar-ban Divisional Forest Officer Bangladesh Forest DepartmentKhulna Bangladesh 2003

[14] J Sambrook and DW RussellMolecular Cloning-A LaboratoryManual Cold Spring Harbor Laboratory Cold Spring HarborNY USA 2001

[15] S Rozen andH J Skaletsky ldquoPrimer3 on theWWWfor generalusers and for biologist programmersrdquo in Bioinformatics Meth-ods and Protocols Methods inMolecular Biology S Krawetz andS Misener Eds pp 365ndash386 Humana Press Totowa NJ USA2000

[16] S F AltschulW GishWMiller EWMyers and D J LipmanldquoBasic local alignment search toolrdquo Journal ofMolecular Biologyvol 215 no 3 pp 403ndash410 1990

[17] F Sanger S Nicklen and A R Coulson ldquoDNA sequencingwith chain-terminating inhibitorsrdquo Proceedings of the NationalAcademy of Sciences of the United States of America vol 74 no12 pp 5463ndash5467 1977

[18] K Tamura D Peterson N Peterson G Stecher M Nei andS Kumar ldquoMEGA5 molecular evolutionary genetics analysisusing maximum likelihood evolutionary distance and max-imum parsimony methodsrdquo Molecular Biology and Evolutionvol 28 no 10 pp 2731ndash2739 2011

[19] L Excoffier and H E L Lischer ldquoArlequin suite ver 35 a newseries of programs to perform population genetics analysesunder Linux and Windowsrdquo Molecular Ecology Resources vol10 no 3 pp 564ndash567 2010

[20] P Librado and J Rozas ldquoDnaSP v5 a software for comprehen-sive analysis of DNA polymorphism datardquo Bioinformatics vol25 no 11 pp 1451ndash1452 2009

[21] M Kimura ldquoA simple method for estimating evolutionary ratesof base substitutions through comparative studies of nucleotidesequencesrdquo Journal ofMolecular Evolution vol 16 no 2 pp 111ndash120 1980

[22] H-J Bandelt P Forster andA Rohl ldquoMedian-joining networksfor inferring intraspecific phylogeniesrdquo Molecular Biology andEvolution vol 16 no 1 pp 37ndash48 1999

[23] S Wright ldquoThe interpretation of population structure by F-statistics with special regard to systems of matingrdquo Evolutionvol 19 no 3 pp 395ndash420 1965

[24] S W Guo and E A Thompson ldquoPerforming the exact testof Hardy-Weinberg proportion for multiple allelesrdquo Biometricsvol 48 no 2 pp 361ndash372 1992

[25] F Tajima ldquoStatistical method for testing the neutral mutationhypothesis by DNApolymorphismrdquoGenetics vol 123 no 3 pp585ndash595 1989

[26] Y-X Fu ldquoStatistical tests of neutrality of mutations againstpopulation growth hitchhiking and background selectionrdquoGenetics vol 147 no 2 pp 915ndash925 1997

[27] W S Grant and B W Bowen ldquoShallow population historiesin deep evolutionary lineages of marine fishes insights fromsardines and anchovies and lessons for conservationrdquo Journalof Heredity vol 89 no 5 pp 415ndash426 1998

Scientifica 9

[28] M A Aboim G M Menezes T Schlitt and A D RogersldquoGenetic structure and history of populations of the deep-seafish Helicolenus dactylopterus (Delaroche 1809) inferred frommtDNA sequence analysisrdquoMolecular Ecology vol 14 no 5 pp1343ndash1354 2005

[29] G D Melvin M J Dadswell and J D Martin ldquoFidelity ofAmerican shad Alosa sapidissima (Clupeidae) to its river ofprevious spawningrdquo Canadian Journal of Fisheries and AquaticSciences vol 43 no 3 pp 640ndash646 1986

[30] C A Reeb L Arcangeli and B A Block ldquoStructure andmigra-tion corridors in Pacific populations of the Swordfish Xiphiusgladius as inferred through analyses of mitochondrial DNArdquoMarine Biology vol 136 no 6 pp 1123ndash1131 2000

[31] B Ely J Vinas J R Alvarado Bremer et al ldquoConsequences ofthe historical demography on the global population structure oftwohighlymigratory cosmopolitanmarine fishesThe yellowfintuna (Thunnus albacares) and the skipjack tuna (Katsuwonuspelamis)rdquo BMC Evolutionary Biology vol 5 2005

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International Journal of

Volume 2014

Zoology

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Molecular Biology International

GenomicsInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioinformaticsAdvances in

Marine BiologyJournal of

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Signal TransductionJournal of

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Microbiology

2 Scientifica

Brahmane et al [8 9] reported more than one stock of Hilsafrom India using RAPD markers and cytochrome b regionSo the baseline information on genetic stocks needs to beauthenticated However several morphometric and molecu-lar studies (RAPD RFLP) were conducted in Bangladesh andIndia but no study was done using mitochondrial D-loop tounderstand population genetic structure and patterns of geneflow of T ilisha [10]

2 Materials and Methods

21 Fish Sampling The present study included 77 specimensof T ilisha from the geographical distribution range in Indianamely rivers draining inBay of Bengal (ie GangaHooghlyandGodavari) as well as fromDiamondHarbour and ParadipPort and rivers draining to Arabian Sea (ie Narmada andTapti) T ilisha was identified and discriminated from T toliand Hilsa kelee based on morphometric and meristic datafollowing Talwar and Jhingran [11] and Fisher andWhitehead[12] The Hilsa body is oblong and compressed with 30ndash33spines like scutes on abdomen Difference between twomajorHilsa species that is T ilisha and T toli is very minute Inthe former dorsal and ventral profile of the body is equallyconvex while in the later abdominal profile is more convexthan that of dorsal Further about 150 to 200 straight toslightly curved gill rakers are present on the lower part of firstarch in T ilisha while in T toli gill rakers are curved and thenumber of gill rakers is 80 to 90 as discussed by Huda andHaque [13] The fish specimens were photographed on graphpapers and meristic counts of the specimens were comparedMuscle and fin tissues were preserved in 95 vv ethanol andthe vouchers were kept in 10 vv formaldehyde Specific andunambiguous code was given to tissue samples and voucherof each fish specimen (Table 1)

22 DNA Extraction PCR Amplification and DNA Sequenc-ing Approximately 50mg of caudal or anal fin or mus-cle tissue was used for DNA isolation following standardphenol chloroform isoamyl alcohol method [14] Precipi-tated DNA was resuspended in TE buffer (10mM Tris-HCl01mM EDTA and pH 8) and concentration was determinedusing Nanodrop 2000 (Thermo Scientific USA)The primersTIDF (51015840-AACTTCCACCCCTAACTCCC-31015840) and TIDR(51015840-GTGCTTGCGGGGCTTG-31015840) were designed usingPrimer3 [15] and BLASTn [16] software of NCBI so as toamplify complete control region of mitochondrial DNA(mtDNA) The PCR reaction of 50 120583L volume contained 1xbuffer 100120583M dNTPs 2mM MgCl

2 10 picomoles of each

primer 3U TaqDNA polymerase and 100 ng template DNAAmplifications were performed in Veriti 96 fast thermalcycler (Applied Biosystems Inc USA) The thermal regimefor control region consisted of initial denaturation of 3minat 94∘C followed by 35 cycles of denaturation at 94∘C for50 sec annealing at 47∘C for 30 sec and extension at 72∘C for80 sec with final extension of 10min at 72∘C PCR productswere visualized on 1 agarose gels stained with ethidiumbromide and documented using a gel documentation system(UVP USA) DNA sequencing was performed followingthe dideoxynucleotide chain termination method [17] using

an automated ABI 3730 sequencer (Applied Biosystems IncUSA)

23 Sequence Analysis Complete control region sequencewas generated from forward and reverse sequence readsusing MEGA 51 [18] Ambiguities were referred againstthe sequencing electropherogramsThe consensus sequenceswere blasted in NCBI for the nearest similar sequencematches using BLASTn and submitted to GenBank (Table 1)DNA sequences were analysed by ClustalW Arlequin version35 [19] DnaSP version 510 [20] and MEGA 51 softwarefor nucleotide composition number of polymorphic sites (119878)haplotype diversity (ℎ) and nucleotide diversity (120587)

The evolutionary history was inferred using the MLmethod The bootstrap consensus tree inferred from 1000replicates is taken to represent the evolutionary history ofthe taxa analysedThe evolutionary distances were computedusing the Kimura 2-parameter method [21] and are in theunits of the number of base substitutions per site Therate variation among sites was modelled with a gammadistribution (shape parameter = 1)

24 Haplotype Analysis Minimum spanning network ofhaplotypes was prepared by Network 46 software [22]Intrapopulation diversity was analysed by estimating hap-lotype diversity which indicates the probability that tworandomly chosen haplotypes are different and nucleotidediversity which indicates the probability that two randomlychosen homologous nucleotides are different

25 Population Genetic Analysis The isolation-by-distanceeffects on population genetic structure were estimated byIBDWS 323 and pairwise 119865ST statistics [23] using Arlequin35 The hierarchical nesting of genetic diversity was esti-mated using the analysis of molecular variance (AMOVA)approach and was calculated using Arlequin 35 Significanceof pairwise population comparisons was tested by 20000permutations The AMOVA tests were organized in a hierar-chical manner and 1000 permutation procedures were usedto construct null distributions and to test the significance ofvariance components [24] To detect population expansionor contraction Tajimarsquos D and Fursquos FS values were estimatedbased on pairwise differences between sequences TajimarsquosD test [25] calculates the distribution of allele frequency ofsegregating sites whereas Fursquos FS test [26] is based on thedistribution of alleles or haplotypes

3 Results

31 Molecular Characterization and Genetic Diversity Table 1shows the number of samples (119873) number of haplotypes(119873Hap) haplotype diversity (Hap119863) and nucleotide diver-sity (120587) for each population A total of 77 individuals weresequenced for the mtDNA control region (873 bp)

Among 77 samples 94 polymorphic sites and 58 hap-lotypes were detected These polymorphisms included 64parsimony informative sites and 32 singleton sites Thenucleotide composition () was 317 (A) 279 (T) 237 (C)and 167 (G)

Scientifica 3

Table1Detailoffi

shsamplingshaplotype

diversitynucleotided

iversityandGenBa

nkaccessionnu

mbersin

Tilisha

Popu

latio

nsSamples

ize

(119873)

Num

bero

fhaplotypes

(119873Hap)

Latitud

elong

itude

GenBa

nkaccessionnu

mbers

Haplotype

diversity

(Hap119863)

Nucleotided

iversity

(120587)

Hoo

ghlyFeeder

CanalFarakk

awestB

engal

88

2448N

8755E

KU214744

-KU214751

1000plusmn000391

001480plusmn000

00027

Diamon

dHarbo

urw

estB

engal

86

2447N

8755E

KU214760-K

U214766

0893plusmn001238

00100

9plusmn000

00030

Ganga

riverbelow

Farakk

aBarrage

Maldaw

estB

engal

77

2251N

8822E

KU214806-K

U214820

1000plusmn000583

001629plusmn000

00049

Narmadar

iverB

arkalGujarat

1312

2210

N8810E

KU214752-K

U214759

0987plusmn000125

001566plusmn000

0006

6Paradippo

rtO

disha

99

2019

N8636E

KU214780-K

U214788

1000plusmn000274

001204plusmn000

00062

God

avaririverR

ajahmun

dry

And

hraP

radesh

87

1656N

814

4EKU

214789-K

U214795

1000plusmn000583

001207plusmn000

0006

8

TaptiriverU

kaid

amSuratG

ujarat

85

2155N

7325E

KU214767-K

U214779

0756plusmn001678

001835plusmn000

00059

Hoo

ghly

KalyaniwestB

engal

1612

2115

N7335E

KU214796-K

U214805

0962plusmn000159

000

935plusmn000

00012

4 Scientifica

The haplotype diversity (ℎ) of the analysed populationswas rather high with observed values between 1000 plusmn 00058in Ganga and 0756 plusmn 001678 in Tapti population Thenucleotide diversity (120587) within each population was very lowranging from 000935 plusmn 00000012 in Hooghly to 001835 plusmn00000059 in Tapti population (Table 1)

32 Haplotype Distribution and Phylogenies In phylogeneticstudy (Figure 1) and minimum spanning network of 58 hap-lotypes (Figure 2) eight populations of T ilishawere groupedin four lineages The haplotypes shared among differentpopulations were 258 and the rest of 742 were privatehaplotypes Hap 8 was the most common haplotype sharedamong Hooghly Feeder Canal Paradip Port Hooghly andDiamond Harbour populations Hap 7 was shared amongParadip Port andHooghly Feeder Canal populations Hap 24andHap 27were shared between Tapti andNarmada popula-tions Hap 1 was shared between Hooghly Feeder Canal andGanga populations Hap 15 was shared betweenHooghly andGanga populations (Table 2)

33 Population Genetic Analysis There is significant geo-graphical structuring among populations only when all pop-ulations were grouped in Bay of Bengal and Arabian SeaAMOVA revealed 442 variation among populations and9558 variation within population (Table 3) which wasfurther supported by significant 119865ST value (ie 00441 119901 lt0001) Estimates of genetic differentiation between eightpopulations using 119865 statistics are given in Table 4 The popu-lations from Narmada and Tapti showed high level of geneticdifferentiation from other populations

Tajimarsquos D was nonsignificantly negative for all exceptNarmada and Tapti populations Fursquos FS test also showednonsignificant negative values for all except Diamond Har-bour and Tapti populations Large differences were observedbetween 120579

0(population before expansion) and 120579

1(population

after expansion) In addition the SSD and Hri values werealso nonsignificant except for Diamond Harbour and Taptipopulations (Table 5) Also low haplotype diversity andmoderate to high nucleotide diversity was observed in Dia-mondHarbour andTapti populationsMismatch distributioncurves were constructed to study the genetic bottleneck andall histograms presented multimodal curves characteristic ofpopulations with constant size over time The plot betweengenetic distance and geographic distances showed a highlysignificant positive correlation indicating that geographicdistance corroborates variation in genetic distance betweenHilsa populations (1199032 = 0230 119901 le 001) (Figure 3)

4 Discussion

The significant positive correlation between geographical andgenetic distances usually referred to as ldquoisolation by distancerdquois typically suggestive of migration-drift equilibrium Ourresult showed high haplotype diversity (high genetic varia-tion) and lownucleotide diversity and significant positive FursquosFS and TajimarsquosD test except for Tapti andDiamondHarbourindicative of recent population expansion after a genetic bot-tleneck or founder events [27 28]

Hap_6Hap_37Hap_17Hap_3Hap_18Hap_36Hap_55Hap_16Hap_35Hap_33Hap_51Hap_57Hap_10Hap_34Hap_53Hap_15Hap_45Hap_32Hap_7Hap_5Hap_54Hap_11Hap_58Hap_4Hap_9Hap_44Hap_49Hap_52Hap_13Hap_2Hap_19Hap_38Hap_50Hap_56Hap_12Hap_14Hap_41Hap_1Hap_42Hap_8Hap_43Hap_39Hap_40Hap_46Hap_24Hap_47Hap_26Hap_48Hap_20Hap_30Hap_28Hap_23Hap_22Hap_25Hap_31Hap_29Hap_21Hap_27

51

53

64

79

55

58

74

5564

5569

63

71

80

6153

5381

100

6789

100 Lineage 4

Lineage 1

Lineage 3

Lineage 2

Figure 1 Molecular phylogenetic analysis of 58 haplotypes of Tilisha constructed by Maximum Likelihood method

In this study 119865 statistics (119865ST) AMOVA haplotype net-work and phylogenetic analysis revealed genetic differentia-tion among the T ilisha populations suggesting that T ilishadoes not have a single panmictic population Brahmane etal [8] separated GangaYamuna rivers stock from HooghlyandNarmada using RAPDmarkers while Brahmane et al [9]reported low genetic diversity and absence of population dif-ferentiation of Hilsa by using cytochrome b region in Ganga

Scientifica 5

Table 2 Relative haplotype frequencies in different populations of T ilisha

Haplotype Hooghly Feeder Canal Diamond Harbour Ganga Narmada Paradip Port Godavari Tapti HooghlyHap 1 0125 0 0143 0 0 0 0 0Hap 2 0125 0 0 0 0 0 0 0Hap 3 0125 0 0 0 0 0 0 0Hap 4 0125 0 0 0 0 0 0 0Hap 5 0125 0 0 0 0 0 0 0Hap 6 0125 0 0 0 0 0 0 0Hap 7 0125 0 0 0 0111 0 0 0Hap 8 0125 0375 0 0 0111 0 0 0066Hap 9 0 0125 0 0 0 0 0 0Hap 10 0 0125 0 0 0 0 0 0Hap 11 0 0125 0 0 0 0 0 0Hap 12 0 0125 0 0 0 0 0 0Hap 13 0 0125 0 0 0 0 0 0Hap 14 0 0 0143 0 0 0 0 0Hap 15 0 0 0143 0 0 0 0 0133Hap 16 0 0 0143 0 0 0 0 0Hap 17 0 0 0143 0 0 0 0 0Hap 18 0 0 0143 0 0 0 0 0Hap 19 0 0 0143 0 0 0 0 0Hap 20 0 0 0 00769 0 0 0 0Hap 21 0 0 0 00769 0 0 0 0Hap 22 0 0 0 00769 0 0 0 0Hap 23 0 0 0 00769 0 0 0 0Hap 24 0 0 0 0154 0 0 01 0Hap 25 0 0 0 00769 0 0 0 0Hap 26 0 0 0 00769 0 0 0 0Hap 27 0 0 0 00769 0 0 05 0Hap 28 0 0 0 00769 0 0 0 0Hap 29 0 0 0 00769 0 0 0 0Hap 30 0 0 0 00769 0 0 0 0Hap 31 0 0 0 00769 0 0 0 0Hap 32 0 0 0 0 0111 0 0 0Hap 33 0 0 0 0 0111 0 0 0Hap 34 0 0 0 0 0111 0 0 0Hap 35 0 0 0 0 0111 0 0 0Hap 36 0 0 0 0 0111 0 0 0Hap 37 0 0 0 0 0111 0 0 0Hap 38 0 0 0 0 0111 0 0 0Hap 39 0 0 0 0 0 0143 0 0Hap 40 0 0 0 0 0 0143 0 0Hap 41 0 0 0 0 0 0143 0 0Hap 42 0 0 0 0 0 0143 0 0Hap 43 0 0 0 0 0 0143 0 0Hap 44 0 0 0 0 0 0143 0 0Hap 45 0 0 0 0 0 0143 0 0Hap 46 0 0 0 0 0 0 02 0Hap 47 0 0 0 0 0 0 01 0Hap 48 0 0 0 0 0 0 01 0Hap 49 0 0 0 0 0 0 0 0066Hap 50 0 0 0 0 0 0 0 0066Hap 51 0 0 0 0 0 0 0 0066Hap 52 0 0 0 0 0 0 0 0066Hap 53 0 0 0 0 0 0 0 0066Hap 54 0 0 0 0 0 0 0 02Hap 55 0 0 0 0 0 0 0 0066Hap 56 0 0 0 0 0 0 0 0066Hap 57 0 0 0 0 0 0 0 0066Hap 58 0 0 0 0 0 0 0 0066

6 Scientifica

H_1

H_2

H_3

H_4

H_5

H_6

H_7H_8

H_9

H_10

H_11

H_12

H_13

H_14

H_15

H_16

H_17

H_18H_19

H_20

H_21

H_22

H_23

H_24

H_25

H_26

H_27

H_28

H_29

H_30

H_31

H_32 H_33

H_34

H_35

H_36

H_37

H_38

H_39H_40H_41

H_42

H_43

H_44

H_45 H_46

H_47

H_48

H_49H_50

H_51

H_52

H_53

H_54

H_55 H_56

H_57

H_58

mv1

mv2mv3

mv4

mv5

mv6 mv7

mv8

mv9

mv10

mv11

mv12 mv13mv14

mv15

mv16mv17

mv18

mv19

mv20

mv21

mv22

mv23mv24

mv25

mv26

Lineage 2

Lineage 3

Lineage 1

Lineage 4

Figure 2 Minimum spanning network of T ilisha based on D-loop haplotypes Haplotypes separated by single lines are one mutation apartand small circles along lines represent missing haplotypes (not sampled or extinct)

minus200minus1000 1399 2199 2999599Geographic distance

minus010

minus007

minus004

minus001

002

005

008

011

014

017

020

Gen

etic

dist

ance

Figure 3 Plot of genetic distance versus geographical distance (inKms) for analysing isolation-by-distance patterns in T ilisha

and Hooghly rivers and also felt the need of advanced geneticmarkers like ATPase 86 nicotinamide dehydrogenase sub-unit 2 (ND2) andmicrosatellite for confirmation Our resultsalso strengthen the presence of more than one stock of Hilsain Indian subcontinent however Ganga and Hooghly FeederCanal and Hooghly populations shared the same genepool

Geographical divergence and gene flow among subpopula-tions can be assessed by phylogenetic analysis and haplotypenetwork provided that gene lineages have accumulated suffi-cient polymorphisms over time and populations have beenisolated completely to allow genetic drift to act In presentstudy ML tree and haplotype network of control region haveclearly distinguished four major cladeshaplogroups namelyLineage 1 (Bay of Bengal populations Diamond HarbourGanga Hooghly Feeder Canal Godavari Paradip Port andHooghly) Lineage 2 (Godavari + some haplotypes from Bayof Bengal population) Lineage 3 (Tapti population) and Lin-eage 4 (Narmada population) Lineage 2 had mixed haplo-types mostly from Godavari and some from Hooghly FeederCanal (Hap 1) DiamondHarbour (Hap 12) Ganga (Hap 14)and common haplotype (Hap 8) this can be explained byslightlymistakenmigration routes taken by some individualsHowever formation of four clades may be explained by thephilopatry driven genetic differentiation that also corrobo-rates previous tagging studies that demonstrated limited nataland breeding dispersal ofHilsa [6]This type of behaviour hasalso been reported in highly studied American shad whichexhibits a high rate of philopatry with 97 of spawnersreturning to their natal stream [29] These results were alsoconfirmed by hierarchical AMOVA analysis that is among-group analysis (two-gene pool analysis of Ganga HooghlyHooghly Feeder Canal Diamond Harbour Paradip PortGodavari and Tapti and Narmada gene pools) having signifi-cant variation of 361 This behavioural isolation of Hilsapopulations was also strongly supported by pairwise signifi-cant 119865ST values of Tapti and Narmada with Godavari ParadipPort Diamond Harbour Hooghly Feeder Canal and Ganga

Scientifica 7

Table 3 Hierarchal analysis of molecular variance (AMOVA) for T ilisha

Source of variation df Sum of squares Variance components Percentage of variationAmong populations 7 4773 002189119881

119886442

Within populations 69 32681 047364119881119887

9558Total 76 37455 049553Fixation index 119865ST 00441

Table 4 Pairwise 119865ST (below diagonal) and associated 119901 values (above diagonal) among T ilisha populations Bold values denote statisticalsignificant values after the Bonferroni correction

Hooghly Feeder Canal Diamond Harbour Ganga Narmada Paradip Port Godavari Tapti HooghlyHooghly Feeder Canal 0 05855 09909 048649 099099 099099 003604 022523Diamond Harbour 00070 0 016216 003604 028829 019820 000000 003604Ganga minus00181 00547 0 050450 099099 099099 003604 053153Narmada 00067 00570lowast 000687 0 044144 062162 003604 003604Paradip Port minus00285 00115 0 00066 0 099099 000000 031532Godavari 0 00547 0 00068 0 0 001802 018018Tapti 0127lowast 01788lowastlowast 01302lowast 00749lowast 01243lowastlowast 01302lowast 0 000000Hooghly 00120 00462lowast 00015 00256lowast 00126 00207 01351lowastlowast 0lowastlowast119901 lt 0001 lowast119901 lt 005

Table 5 Genetic diversity indices and demographic parameters of T ilisha

Population ti tv 1205790

1205791

Tajimarsquos119863 Fursquos 119865119878 Hri SSDHooghly 26 1 7490 99999 minus007622 minus27044 004227 001401Diamond Harbour 22 1 7200 99999 minus005012 078291 016709 010294lowast

Hooghly Feeder Canal 31 6 4826 99999 minus051513 minus16082 012245 004966Paradip Port 31 6 20202 99999 minus115597 minus26925 002623 002041Godavari 22 9 2638 99999 minus080192 minus14708 009524 005183Ganga 30 6 12179 99999 minus019653 minus09430 024943 007201Narmada 37 2 17159 201765 041084 minus23072 002548 002592Tapti 27 6 12237 99999 199567 555599 013975 011588lowastlowast119901 lt 005

Although phylogenetic and phylogeographic analysis (mini-mum spanning network) shows that genetic variation is notrandomly distributed among Bay of Bengal rivers a patternof population structure and gene flow has been difficultto verify statistically Analysis of pairwise population 119865STprovides strong support for divergence betweenBay of Bengaland Arabian Sea population however biologically acceptedlevel of statistical significance (119901 le 005) is too stringent toreveal the subtle genetic differences within Bay of Bengal andArabian Sea populations when the Bonferroni correction formultiple 119901 value is applied [30]

The genetic marker most commonly used to elucidatepopulation structure in fish has been control region andwas considered sensitive enough to test for structure amongmost populations due to rapid accumulation of mutationin the control region as well as the simplicity of mtDNAinheritance through maternal line [3] However its suitabil-ity is questionable at interbasin level as true relationship

between populations was masked by high haplotype diversityand homoplasy 119865 statistics (119865ST) and AMOVA could notclearly demarcate the population structure among popu-lations from Bay of Bengal Mazumder and Alam [10]used RFLP of mitochondrial D-loop region to differen-tiate riverine estuarine and marine stocks and observedsignificant differentiation between the riverine and marine(Coxrsquos Bazar) populations but not between the marine andone of the estuarine populations as electrophoretic analysisof PCR-RFLP has lower resolving power than sequencingof the same PCR product They suggested sequencing ofD-loop region and faster evolving molecular markers forpopulation structure studies such as microsatellite lociSimilar results were recorded for yellowfin tuna whereanalysis of the control region between Atlantic and Indo-Pacific populations was unable to detect structure but clearlyresolved by PCR RFLP of the ATPase 68 and COI III genes[31]

8 Scientifica

5 Conclusion

The populations are supposed to be separated by the philo-patric behaviour Phylogeographical structure defined byAMOVA and region specific haplotypes distinguished pop-ulations up to sea level (Bay of Bengal and Arabian sea) butpopulation structuring at basin level was not noticed usingD-loop as a marker so we suggest the use of somemoderatelyevolvingmarkers such as ATPase 86 nicotinamide dehydro-genase subunit 2 (ND2) and microsatellites for elucidatingpopulation structure of Hilsa

Disclosure

Theauthors declare that all the experiments performed in thisstudy are in compliancewith the current laws of their country

Competing Interests

The authors declare that there are no competing interestsregarding the publication of this paper

Acknowledgments

The authors are thankful to the Director of National Bureauof Fish Genetic Resources for support and guidance

References

[1] S R Pillay and H Rosa Jr ldquoSynopsis of biological data on hilsaHilsa ilisha (Hamilton 1882)rdquo FAO Fisheries Biology Synopsis25 Rome Italy 1963

[2] W S Lakra V Mohindra and K K Lal ldquoFish genetics andconservation research in India status and perspectivesrdquo FishPhysiology and Biochemistry vol 33 no 4 pp 475ndash487 2007

[3] J C Avise Molecular Markers Natural History and EvolutionChapman and Hall New York NY USA 1994

[4] J Avise J Arnold R Ball et al ldquoIntraspecific phylogeographythe mitochondrial DNA bridge between population geneticsand systematicsrdquo Annual Review of Ecology Evolution andSystematics vol 18 pp 489ndash522 1987

[5] H M Bradman P M Grewe and B Appleton ldquoDirect com-parison of mitochondrial markers for the analysis of swordfishpopulation structurerdquo Fisheries Research vol 109 no 1 pp 95ndash99 2011

[6] S R Pillay K V Rao and P K Mathur ldquoPreliminary report onthe tagging of the HilsaHilsa ilisha (Hamilton)rdquo Proceedings ofthe Indo-Pacific Fisheries Council India 1962

[7] A N Ghosh R K Bhattacharya and K V Rao ldquoOn the iden-tification of the sub-populations of Hilsa ilisha (Ham) in theGangetic system with a note on their distributionrdquo Proceedingsof the National Academy of Sciences India Section B BiologicalSciences vol 34 pp 44ndash57 1968

[8] M P Brahmane M K Das M R Sinha et al ldquoUse of RAPDfingerprinting for delineating populations of hilsa shad Tenu-alosa ilisha (Hamilton 1822)rdquo Genetics and Molecular Researchvol 5 no 4 pp 643ndash652 2006

[9] M P Brahmane S N Kundu M K Das and A P SharmaldquoLowgenetic diversity and absence of population differentiationof hilsa (Tenualosa ilisha) revealed bymitochondrial DNA cyto-chrome b region in Ganga and Hooghly riversrdquo African Journalof Biotechnology vol 12 no 22 pp 3383ndash3389 2013

[10] S K Mazumder and M S Alam ldquoHigh levels of genetic vari-ability and differentiation in hilsa shad Tenualosa ilisha (Clu-peidae Clupeiformes) populations revealed byPCR-RFLP anal-ysis of the mitochondrial DNA D-loop regionrdquo Genetics andMolecular Biology vol 32 no 1 pp 190ndash196 2009

[11] K Talwar and A G Jhingran Inland Fishes of India and Adja-cent Countries Balkema Rotterdam The Netherlands 1991

[12] W Fisher and P J P Whitehead FAO Species IdentificationSheets Fishing Area 57 and 71 FAO Rome Italy 1974

[13] S M Huda andM E Haque Field Guide to Finfishes of Sundar-ban Divisional Forest Officer Bangladesh Forest DepartmentKhulna Bangladesh 2003

[14] J Sambrook and DW RussellMolecular Cloning-A LaboratoryManual Cold Spring Harbor Laboratory Cold Spring HarborNY USA 2001

[15] S Rozen andH J Skaletsky ldquoPrimer3 on theWWWfor generalusers and for biologist programmersrdquo in Bioinformatics Meth-ods and Protocols Methods inMolecular Biology S Krawetz andS Misener Eds pp 365ndash386 Humana Press Totowa NJ USA2000

[16] S F AltschulW GishWMiller EWMyers and D J LipmanldquoBasic local alignment search toolrdquo Journal ofMolecular Biologyvol 215 no 3 pp 403ndash410 1990

[17] F Sanger S Nicklen and A R Coulson ldquoDNA sequencingwith chain-terminating inhibitorsrdquo Proceedings of the NationalAcademy of Sciences of the United States of America vol 74 no12 pp 5463ndash5467 1977

[18] K Tamura D Peterson N Peterson G Stecher M Nei andS Kumar ldquoMEGA5 molecular evolutionary genetics analysisusing maximum likelihood evolutionary distance and max-imum parsimony methodsrdquo Molecular Biology and Evolutionvol 28 no 10 pp 2731ndash2739 2011

[19] L Excoffier and H E L Lischer ldquoArlequin suite ver 35 a newseries of programs to perform population genetics analysesunder Linux and Windowsrdquo Molecular Ecology Resources vol10 no 3 pp 564ndash567 2010

[20] P Librado and J Rozas ldquoDnaSP v5 a software for comprehen-sive analysis of DNA polymorphism datardquo Bioinformatics vol25 no 11 pp 1451ndash1452 2009

[21] M Kimura ldquoA simple method for estimating evolutionary ratesof base substitutions through comparative studies of nucleotidesequencesrdquo Journal ofMolecular Evolution vol 16 no 2 pp 111ndash120 1980

[22] H-J Bandelt P Forster andA Rohl ldquoMedian-joining networksfor inferring intraspecific phylogeniesrdquo Molecular Biology andEvolution vol 16 no 1 pp 37ndash48 1999

[23] S Wright ldquoThe interpretation of population structure by F-statistics with special regard to systems of matingrdquo Evolutionvol 19 no 3 pp 395ndash420 1965

[24] S W Guo and E A Thompson ldquoPerforming the exact testof Hardy-Weinberg proportion for multiple allelesrdquo Biometricsvol 48 no 2 pp 361ndash372 1992

[25] F Tajima ldquoStatistical method for testing the neutral mutationhypothesis by DNApolymorphismrdquoGenetics vol 123 no 3 pp585ndash595 1989

[26] Y-X Fu ldquoStatistical tests of neutrality of mutations againstpopulation growth hitchhiking and background selectionrdquoGenetics vol 147 no 2 pp 915ndash925 1997

[27] W S Grant and B W Bowen ldquoShallow population historiesin deep evolutionary lineages of marine fishes insights fromsardines and anchovies and lessons for conservationrdquo Journalof Heredity vol 89 no 5 pp 415ndash426 1998

Scientifica 9

[28] M A Aboim G M Menezes T Schlitt and A D RogersldquoGenetic structure and history of populations of the deep-seafish Helicolenus dactylopterus (Delaroche 1809) inferred frommtDNA sequence analysisrdquoMolecular Ecology vol 14 no 5 pp1343ndash1354 2005

[29] G D Melvin M J Dadswell and J D Martin ldquoFidelity ofAmerican shad Alosa sapidissima (Clupeidae) to its river ofprevious spawningrdquo Canadian Journal of Fisheries and AquaticSciences vol 43 no 3 pp 640ndash646 1986

[30] C A Reeb L Arcangeli and B A Block ldquoStructure andmigra-tion corridors in Pacific populations of the Swordfish Xiphiusgladius as inferred through analyses of mitochondrial DNArdquoMarine Biology vol 136 no 6 pp 1123ndash1131 2000

[31] B Ely J Vinas J R Alvarado Bremer et al ldquoConsequences ofthe historical demography on the global population structure oftwohighlymigratory cosmopolitanmarine fishesThe yellowfintuna (Thunnus albacares) and the skipjack tuna (Katsuwonuspelamis)rdquo BMC Evolutionary Biology vol 5 2005

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Anatomy Research International

PeptidesInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

International Journal of

Volume 2014

Zoology

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Molecular Biology International

GenomicsInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioinformaticsAdvances in

Marine BiologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Signal TransductionJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

Evolutionary BiologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Biochemistry Research International

ArchaeaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Genetics Research International

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Virolog y

Hindawi Publishing Corporationhttpwwwhindawicom

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Enzyme Research

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Microbiology

Scientifica 3

Table1Detailoffi

shsamplingshaplotype

diversitynucleotided

iversityandGenBa

nkaccessionnu

mbersin

Tilisha

Popu

latio

nsSamples

ize

(119873)

Num

bero

fhaplotypes

(119873Hap)

Latitud

elong

itude

GenBa

nkaccessionnu

mbers

Haplotype

diversity

(Hap119863)

Nucleotided

iversity

(120587)

Hoo

ghlyFeeder

CanalFarakk

awestB

engal

88

2448N

8755E

KU214744

-KU214751

1000plusmn000391

001480plusmn000

00027

Diamon

dHarbo

urw

estB

engal

86

2447N

8755E

KU214760-K

U214766

0893plusmn001238

00100

9plusmn000

00030

Ganga

riverbelow

Farakk

aBarrage

Maldaw

estB

engal

77

2251N

8822E

KU214806-K

U214820

1000plusmn000583

001629plusmn000

00049

Narmadar

iverB

arkalGujarat

1312

2210

N8810E

KU214752-K

U214759

0987plusmn000125

001566plusmn000

0006

6Paradippo

rtO

disha

99

2019

N8636E

KU214780-K

U214788

1000plusmn000274

001204plusmn000

00062

God

avaririverR

ajahmun

dry

And

hraP

radesh

87

1656N

814

4EKU

214789-K

U214795

1000plusmn000583

001207plusmn000

0006

8

TaptiriverU

kaid

amSuratG

ujarat

85

2155N

7325E

KU214767-K

U214779

0756plusmn001678

001835plusmn000

00059

Hoo

ghly

KalyaniwestB

engal

1612

2115

N7335E

KU214796-K

U214805

0962plusmn000159

000

935plusmn000

00012

4 Scientifica

The haplotype diversity (ℎ) of the analysed populationswas rather high with observed values between 1000 plusmn 00058in Ganga and 0756 plusmn 001678 in Tapti population Thenucleotide diversity (120587) within each population was very lowranging from 000935 plusmn 00000012 in Hooghly to 001835 plusmn00000059 in Tapti population (Table 1)

32 Haplotype Distribution and Phylogenies In phylogeneticstudy (Figure 1) and minimum spanning network of 58 hap-lotypes (Figure 2) eight populations of T ilishawere groupedin four lineages The haplotypes shared among differentpopulations were 258 and the rest of 742 were privatehaplotypes Hap 8 was the most common haplotype sharedamong Hooghly Feeder Canal Paradip Port Hooghly andDiamond Harbour populations Hap 7 was shared amongParadip Port andHooghly Feeder Canal populations Hap 24andHap 27were shared between Tapti andNarmada popula-tions Hap 1 was shared between Hooghly Feeder Canal andGanga populations Hap 15 was shared betweenHooghly andGanga populations (Table 2)

33 Population Genetic Analysis There is significant geo-graphical structuring among populations only when all pop-ulations were grouped in Bay of Bengal and Arabian SeaAMOVA revealed 442 variation among populations and9558 variation within population (Table 3) which wasfurther supported by significant 119865ST value (ie 00441 119901 lt0001) Estimates of genetic differentiation between eightpopulations using 119865 statistics are given in Table 4 The popu-lations from Narmada and Tapti showed high level of geneticdifferentiation from other populations

Tajimarsquos D was nonsignificantly negative for all exceptNarmada and Tapti populations Fursquos FS test also showednonsignificant negative values for all except Diamond Har-bour and Tapti populations Large differences were observedbetween 120579

0(population before expansion) and 120579

1(population

after expansion) In addition the SSD and Hri values werealso nonsignificant except for Diamond Harbour and Taptipopulations (Table 5) Also low haplotype diversity andmoderate to high nucleotide diversity was observed in Dia-mondHarbour andTapti populationsMismatch distributioncurves were constructed to study the genetic bottleneck andall histograms presented multimodal curves characteristic ofpopulations with constant size over time The plot betweengenetic distance and geographic distances showed a highlysignificant positive correlation indicating that geographicdistance corroborates variation in genetic distance betweenHilsa populations (1199032 = 0230 119901 le 001) (Figure 3)

4 Discussion

The significant positive correlation between geographical andgenetic distances usually referred to as ldquoisolation by distancerdquois typically suggestive of migration-drift equilibrium Ourresult showed high haplotype diversity (high genetic varia-tion) and lownucleotide diversity and significant positive FursquosFS and TajimarsquosD test except for Tapti andDiamondHarbourindicative of recent population expansion after a genetic bot-tleneck or founder events [27 28]

Hap_6Hap_37Hap_17Hap_3Hap_18Hap_36Hap_55Hap_16Hap_35Hap_33Hap_51Hap_57Hap_10Hap_34Hap_53Hap_15Hap_45Hap_32Hap_7Hap_5Hap_54Hap_11Hap_58Hap_4Hap_9Hap_44Hap_49Hap_52Hap_13Hap_2Hap_19Hap_38Hap_50Hap_56Hap_12Hap_14Hap_41Hap_1Hap_42Hap_8Hap_43Hap_39Hap_40Hap_46Hap_24Hap_47Hap_26Hap_48Hap_20Hap_30Hap_28Hap_23Hap_22Hap_25Hap_31Hap_29Hap_21Hap_27

51

53

64

79

55

58

74

5564

5569

63

71

80

6153

5381

100

6789

100 Lineage 4

Lineage 1

Lineage 3

Lineage 2

Figure 1 Molecular phylogenetic analysis of 58 haplotypes of Tilisha constructed by Maximum Likelihood method

In this study 119865 statistics (119865ST) AMOVA haplotype net-work and phylogenetic analysis revealed genetic differentia-tion among the T ilisha populations suggesting that T ilishadoes not have a single panmictic population Brahmane etal [8] separated GangaYamuna rivers stock from HooghlyandNarmada using RAPDmarkers while Brahmane et al [9]reported low genetic diversity and absence of population dif-ferentiation of Hilsa by using cytochrome b region in Ganga

Scientifica 5

Table 2 Relative haplotype frequencies in different populations of T ilisha

Haplotype Hooghly Feeder Canal Diamond Harbour Ganga Narmada Paradip Port Godavari Tapti HooghlyHap 1 0125 0 0143 0 0 0 0 0Hap 2 0125 0 0 0 0 0 0 0Hap 3 0125 0 0 0 0 0 0 0Hap 4 0125 0 0 0 0 0 0 0Hap 5 0125 0 0 0 0 0 0 0Hap 6 0125 0 0 0 0 0 0 0Hap 7 0125 0 0 0 0111 0 0 0Hap 8 0125 0375 0 0 0111 0 0 0066Hap 9 0 0125 0 0 0 0 0 0Hap 10 0 0125 0 0 0 0 0 0Hap 11 0 0125 0 0 0 0 0 0Hap 12 0 0125 0 0 0 0 0 0Hap 13 0 0125 0 0 0 0 0 0Hap 14 0 0 0143 0 0 0 0 0Hap 15 0 0 0143 0 0 0 0 0133Hap 16 0 0 0143 0 0 0 0 0Hap 17 0 0 0143 0 0 0 0 0Hap 18 0 0 0143 0 0 0 0 0Hap 19 0 0 0143 0 0 0 0 0Hap 20 0 0 0 00769 0 0 0 0Hap 21 0 0 0 00769 0 0 0 0Hap 22 0 0 0 00769 0 0 0 0Hap 23 0 0 0 00769 0 0 0 0Hap 24 0 0 0 0154 0 0 01 0Hap 25 0 0 0 00769 0 0 0 0Hap 26 0 0 0 00769 0 0 0 0Hap 27 0 0 0 00769 0 0 05 0Hap 28 0 0 0 00769 0 0 0 0Hap 29 0 0 0 00769 0 0 0 0Hap 30 0 0 0 00769 0 0 0 0Hap 31 0 0 0 00769 0 0 0 0Hap 32 0 0 0 0 0111 0 0 0Hap 33 0 0 0 0 0111 0 0 0Hap 34 0 0 0 0 0111 0 0 0Hap 35 0 0 0 0 0111 0 0 0Hap 36 0 0 0 0 0111 0 0 0Hap 37 0 0 0 0 0111 0 0 0Hap 38 0 0 0 0 0111 0 0 0Hap 39 0 0 0 0 0 0143 0 0Hap 40 0 0 0 0 0 0143 0 0Hap 41 0 0 0 0 0 0143 0 0Hap 42 0 0 0 0 0 0143 0 0Hap 43 0 0 0 0 0 0143 0 0Hap 44 0 0 0 0 0 0143 0 0Hap 45 0 0 0 0 0 0143 0 0Hap 46 0 0 0 0 0 0 02 0Hap 47 0 0 0 0 0 0 01 0Hap 48 0 0 0 0 0 0 01 0Hap 49 0 0 0 0 0 0 0 0066Hap 50 0 0 0 0 0 0 0 0066Hap 51 0 0 0 0 0 0 0 0066Hap 52 0 0 0 0 0 0 0 0066Hap 53 0 0 0 0 0 0 0 0066Hap 54 0 0 0 0 0 0 0 02Hap 55 0 0 0 0 0 0 0 0066Hap 56 0 0 0 0 0 0 0 0066Hap 57 0 0 0 0 0 0 0 0066Hap 58 0 0 0 0 0 0 0 0066

6 Scientifica

H_1

H_2

H_3

H_4

H_5

H_6

H_7H_8

H_9

H_10

H_11

H_12

H_13

H_14

H_15

H_16

H_17

H_18H_19

H_20

H_21

H_22

H_23

H_24

H_25

H_26

H_27

H_28

H_29

H_30

H_31

H_32 H_33

H_34

H_35

H_36

H_37

H_38

H_39H_40H_41

H_42

H_43

H_44

H_45 H_46

H_47

H_48

H_49H_50

H_51

H_52

H_53

H_54

H_55 H_56

H_57

H_58

mv1

mv2mv3

mv4

mv5

mv6 mv7

mv8

mv9

mv10

mv11

mv12 mv13mv14

mv15

mv16mv17

mv18

mv19

mv20

mv21

mv22

mv23mv24

mv25

mv26

Lineage 2

Lineage 3

Lineage 1

Lineage 4

Figure 2 Minimum spanning network of T ilisha based on D-loop haplotypes Haplotypes separated by single lines are one mutation apartand small circles along lines represent missing haplotypes (not sampled or extinct)

minus200minus1000 1399 2199 2999599Geographic distance

minus010

minus007

minus004

minus001

002

005

008

011

014

017

020

Gen

etic

dist

ance

Figure 3 Plot of genetic distance versus geographical distance (inKms) for analysing isolation-by-distance patterns in T ilisha

and Hooghly rivers and also felt the need of advanced geneticmarkers like ATPase 86 nicotinamide dehydrogenase sub-unit 2 (ND2) andmicrosatellite for confirmation Our resultsalso strengthen the presence of more than one stock of Hilsain Indian subcontinent however Ganga and Hooghly FeederCanal and Hooghly populations shared the same genepool

Geographical divergence and gene flow among subpopula-tions can be assessed by phylogenetic analysis and haplotypenetwork provided that gene lineages have accumulated suffi-cient polymorphisms over time and populations have beenisolated completely to allow genetic drift to act In presentstudy ML tree and haplotype network of control region haveclearly distinguished four major cladeshaplogroups namelyLineage 1 (Bay of Bengal populations Diamond HarbourGanga Hooghly Feeder Canal Godavari Paradip Port andHooghly) Lineage 2 (Godavari + some haplotypes from Bayof Bengal population) Lineage 3 (Tapti population) and Lin-eage 4 (Narmada population) Lineage 2 had mixed haplo-types mostly from Godavari and some from Hooghly FeederCanal (Hap 1) DiamondHarbour (Hap 12) Ganga (Hap 14)and common haplotype (Hap 8) this can be explained byslightlymistakenmigration routes taken by some individualsHowever formation of four clades may be explained by thephilopatry driven genetic differentiation that also corrobo-rates previous tagging studies that demonstrated limited nataland breeding dispersal ofHilsa [6]This type of behaviour hasalso been reported in highly studied American shad whichexhibits a high rate of philopatry with 97 of spawnersreturning to their natal stream [29] These results were alsoconfirmed by hierarchical AMOVA analysis that is among-group analysis (two-gene pool analysis of Ganga HooghlyHooghly Feeder Canal Diamond Harbour Paradip PortGodavari and Tapti and Narmada gene pools) having signifi-cant variation of 361 This behavioural isolation of Hilsapopulations was also strongly supported by pairwise signifi-cant 119865ST values of Tapti and Narmada with Godavari ParadipPort Diamond Harbour Hooghly Feeder Canal and Ganga

Scientifica 7

Table 3 Hierarchal analysis of molecular variance (AMOVA) for T ilisha

Source of variation df Sum of squares Variance components Percentage of variationAmong populations 7 4773 002189119881

119886442

Within populations 69 32681 047364119881119887

9558Total 76 37455 049553Fixation index 119865ST 00441

Table 4 Pairwise 119865ST (below diagonal) and associated 119901 values (above diagonal) among T ilisha populations Bold values denote statisticalsignificant values after the Bonferroni correction

Hooghly Feeder Canal Diamond Harbour Ganga Narmada Paradip Port Godavari Tapti HooghlyHooghly Feeder Canal 0 05855 09909 048649 099099 099099 003604 022523Diamond Harbour 00070 0 016216 003604 028829 019820 000000 003604Ganga minus00181 00547 0 050450 099099 099099 003604 053153Narmada 00067 00570lowast 000687 0 044144 062162 003604 003604Paradip Port minus00285 00115 0 00066 0 099099 000000 031532Godavari 0 00547 0 00068 0 0 001802 018018Tapti 0127lowast 01788lowastlowast 01302lowast 00749lowast 01243lowastlowast 01302lowast 0 000000Hooghly 00120 00462lowast 00015 00256lowast 00126 00207 01351lowastlowast 0lowastlowast119901 lt 0001 lowast119901 lt 005

Table 5 Genetic diversity indices and demographic parameters of T ilisha

Population ti tv 1205790

1205791

Tajimarsquos119863 Fursquos 119865119878 Hri SSDHooghly 26 1 7490 99999 minus007622 minus27044 004227 001401Diamond Harbour 22 1 7200 99999 minus005012 078291 016709 010294lowast

Hooghly Feeder Canal 31 6 4826 99999 minus051513 minus16082 012245 004966Paradip Port 31 6 20202 99999 minus115597 minus26925 002623 002041Godavari 22 9 2638 99999 minus080192 minus14708 009524 005183Ganga 30 6 12179 99999 minus019653 minus09430 024943 007201Narmada 37 2 17159 201765 041084 minus23072 002548 002592Tapti 27 6 12237 99999 199567 555599 013975 011588lowastlowast119901 lt 005

Although phylogenetic and phylogeographic analysis (mini-mum spanning network) shows that genetic variation is notrandomly distributed among Bay of Bengal rivers a patternof population structure and gene flow has been difficultto verify statistically Analysis of pairwise population 119865STprovides strong support for divergence betweenBay of Bengaland Arabian Sea population however biologically acceptedlevel of statistical significance (119901 le 005) is too stringent toreveal the subtle genetic differences within Bay of Bengal andArabian Sea populations when the Bonferroni correction formultiple 119901 value is applied [30]

The genetic marker most commonly used to elucidatepopulation structure in fish has been control region andwas considered sensitive enough to test for structure amongmost populations due to rapid accumulation of mutationin the control region as well as the simplicity of mtDNAinheritance through maternal line [3] However its suitabil-ity is questionable at interbasin level as true relationship

between populations was masked by high haplotype diversityand homoplasy 119865 statistics (119865ST) and AMOVA could notclearly demarcate the population structure among popu-lations from Bay of Bengal Mazumder and Alam [10]used RFLP of mitochondrial D-loop region to differen-tiate riverine estuarine and marine stocks and observedsignificant differentiation between the riverine and marine(Coxrsquos Bazar) populations but not between the marine andone of the estuarine populations as electrophoretic analysisof PCR-RFLP has lower resolving power than sequencingof the same PCR product They suggested sequencing ofD-loop region and faster evolving molecular markers forpopulation structure studies such as microsatellite lociSimilar results were recorded for yellowfin tuna whereanalysis of the control region between Atlantic and Indo-Pacific populations was unable to detect structure but clearlyresolved by PCR RFLP of the ATPase 68 and COI III genes[31]

8 Scientifica

5 Conclusion

The populations are supposed to be separated by the philo-patric behaviour Phylogeographical structure defined byAMOVA and region specific haplotypes distinguished pop-ulations up to sea level (Bay of Bengal and Arabian sea) butpopulation structuring at basin level was not noticed usingD-loop as a marker so we suggest the use of somemoderatelyevolvingmarkers such as ATPase 86 nicotinamide dehydro-genase subunit 2 (ND2) and microsatellites for elucidatingpopulation structure of Hilsa

Disclosure

Theauthors declare that all the experiments performed in thisstudy are in compliancewith the current laws of their country

Competing Interests

The authors declare that there are no competing interestsregarding the publication of this paper

Acknowledgments

The authors are thankful to the Director of National Bureauof Fish Genetic Resources for support and guidance

References

[1] S R Pillay and H Rosa Jr ldquoSynopsis of biological data on hilsaHilsa ilisha (Hamilton 1882)rdquo FAO Fisheries Biology Synopsis25 Rome Italy 1963

[2] W S Lakra V Mohindra and K K Lal ldquoFish genetics andconservation research in India status and perspectivesrdquo FishPhysiology and Biochemistry vol 33 no 4 pp 475ndash487 2007

[3] J C Avise Molecular Markers Natural History and EvolutionChapman and Hall New York NY USA 1994

[4] J Avise J Arnold R Ball et al ldquoIntraspecific phylogeographythe mitochondrial DNA bridge between population geneticsand systematicsrdquo Annual Review of Ecology Evolution andSystematics vol 18 pp 489ndash522 1987

[5] H M Bradman P M Grewe and B Appleton ldquoDirect com-parison of mitochondrial markers for the analysis of swordfishpopulation structurerdquo Fisheries Research vol 109 no 1 pp 95ndash99 2011

[6] S R Pillay K V Rao and P K Mathur ldquoPreliminary report onthe tagging of the HilsaHilsa ilisha (Hamilton)rdquo Proceedings ofthe Indo-Pacific Fisheries Council India 1962

[7] A N Ghosh R K Bhattacharya and K V Rao ldquoOn the iden-tification of the sub-populations of Hilsa ilisha (Ham) in theGangetic system with a note on their distributionrdquo Proceedingsof the National Academy of Sciences India Section B BiologicalSciences vol 34 pp 44ndash57 1968

[8] M P Brahmane M K Das M R Sinha et al ldquoUse of RAPDfingerprinting for delineating populations of hilsa shad Tenu-alosa ilisha (Hamilton 1822)rdquo Genetics and Molecular Researchvol 5 no 4 pp 643ndash652 2006

[9] M P Brahmane S N Kundu M K Das and A P SharmaldquoLowgenetic diversity and absence of population differentiationof hilsa (Tenualosa ilisha) revealed bymitochondrial DNA cyto-chrome b region in Ganga and Hooghly riversrdquo African Journalof Biotechnology vol 12 no 22 pp 3383ndash3389 2013

[10] S K Mazumder and M S Alam ldquoHigh levels of genetic vari-ability and differentiation in hilsa shad Tenualosa ilisha (Clu-peidae Clupeiformes) populations revealed byPCR-RFLP anal-ysis of the mitochondrial DNA D-loop regionrdquo Genetics andMolecular Biology vol 32 no 1 pp 190ndash196 2009

[11] K Talwar and A G Jhingran Inland Fishes of India and Adja-cent Countries Balkema Rotterdam The Netherlands 1991

[12] W Fisher and P J P Whitehead FAO Species IdentificationSheets Fishing Area 57 and 71 FAO Rome Italy 1974

[13] S M Huda andM E Haque Field Guide to Finfishes of Sundar-ban Divisional Forest Officer Bangladesh Forest DepartmentKhulna Bangladesh 2003

[14] J Sambrook and DW RussellMolecular Cloning-A LaboratoryManual Cold Spring Harbor Laboratory Cold Spring HarborNY USA 2001

[15] S Rozen andH J Skaletsky ldquoPrimer3 on theWWWfor generalusers and for biologist programmersrdquo in Bioinformatics Meth-ods and Protocols Methods inMolecular Biology S Krawetz andS Misener Eds pp 365ndash386 Humana Press Totowa NJ USA2000

[16] S F AltschulW GishWMiller EWMyers and D J LipmanldquoBasic local alignment search toolrdquo Journal ofMolecular Biologyvol 215 no 3 pp 403ndash410 1990

[17] F Sanger S Nicklen and A R Coulson ldquoDNA sequencingwith chain-terminating inhibitorsrdquo Proceedings of the NationalAcademy of Sciences of the United States of America vol 74 no12 pp 5463ndash5467 1977

[18] K Tamura D Peterson N Peterson G Stecher M Nei andS Kumar ldquoMEGA5 molecular evolutionary genetics analysisusing maximum likelihood evolutionary distance and max-imum parsimony methodsrdquo Molecular Biology and Evolutionvol 28 no 10 pp 2731ndash2739 2011

[19] L Excoffier and H E L Lischer ldquoArlequin suite ver 35 a newseries of programs to perform population genetics analysesunder Linux and Windowsrdquo Molecular Ecology Resources vol10 no 3 pp 564ndash567 2010

[20] P Librado and J Rozas ldquoDnaSP v5 a software for comprehen-sive analysis of DNA polymorphism datardquo Bioinformatics vol25 no 11 pp 1451ndash1452 2009

[21] M Kimura ldquoA simple method for estimating evolutionary ratesof base substitutions through comparative studies of nucleotidesequencesrdquo Journal ofMolecular Evolution vol 16 no 2 pp 111ndash120 1980

[22] H-J Bandelt P Forster andA Rohl ldquoMedian-joining networksfor inferring intraspecific phylogeniesrdquo Molecular Biology andEvolution vol 16 no 1 pp 37ndash48 1999

[23] S Wright ldquoThe interpretation of population structure by F-statistics with special regard to systems of matingrdquo Evolutionvol 19 no 3 pp 395ndash420 1965

[24] S W Guo and E A Thompson ldquoPerforming the exact testof Hardy-Weinberg proportion for multiple allelesrdquo Biometricsvol 48 no 2 pp 361ndash372 1992

[25] F Tajima ldquoStatistical method for testing the neutral mutationhypothesis by DNApolymorphismrdquoGenetics vol 123 no 3 pp585ndash595 1989

[26] Y-X Fu ldquoStatistical tests of neutrality of mutations againstpopulation growth hitchhiking and background selectionrdquoGenetics vol 147 no 2 pp 915ndash925 1997

[27] W S Grant and B W Bowen ldquoShallow population historiesin deep evolutionary lineages of marine fishes insights fromsardines and anchovies and lessons for conservationrdquo Journalof Heredity vol 89 no 5 pp 415ndash426 1998

Scientifica 9

[28] M A Aboim G M Menezes T Schlitt and A D RogersldquoGenetic structure and history of populations of the deep-seafish Helicolenus dactylopterus (Delaroche 1809) inferred frommtDNA sequence analysisrdquoMolecular Ecology vol 14 no 5 pp1343ndash1354 2005

[29] G D Melvin M J Dadswell and J D Martin ldquoFidelity ofAmerican shad Alosa sapidissima (Clupeidae) to its river ofprevious spawningrdquo Canadian Journal of Fisheries and AquaticSciences vol 43 no 3 pp 640ndash646 1986

[30] C A Reeb L Arcangeli and B A Block ldquoStructure andmigra-tion corridors in Pacific populations of the Swordfish Xiphiusgladius as inferred through analyses of mitochondrial DNArdquoMarine Biology vol 136 no 6 pp 1123ndash1131 2000

[31] B Ely J Vinas J R Alvarado Bremer et al ldquoConsequences ofthe historical demography on the global population structure oftwohighlymigratory cosmopolitanmarine fishesThe yellowfintuna (Thunnus albacares) and the skipjack tuna (Katsuwonuspelamis)rdquo BMC Evolutionary Biology vol 5 2005

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Anatomy Research International

PeptidesInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

International Journal of

Volume 2014

Zoology

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Molecular Biology International

GenomicsInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioinformaticsAdvances in

Marine BiologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Signal TransductionJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

Evolutionary BiologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Biochemistry Research International

ArchaeaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Genetics Research International

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Virolog y

Hindawi Publishing Corporationhttpwwwhindawicom

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Enzyme Research

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Microbiology

4 Scientifica

The haplotype diversity (ℎ) of the analysed populationswas rather high with observed values between 1000 plusmn 00058in Ganga and 0756 plusmn 001678 in Tapti population Thenucleotide diversity (120587) within each population was very lowranging from 000935 plusmn 00000012 in Hooghly to 001835 plusmn00000059 in Tapti population (Table 1)

32 Haplotype Distribution and Phylogenies In phylogeneticstudy (Figure 1) and minimum spanning network of 58 hap-lotypes (Figure 2) eight populations of T ilishawere groupedin four lineages The haplotypes shared among differentpopulations were 258 and the rest of 742 were privatehaplotypes Hap 8 was the most common haplotype sharedamong Hooghly Feeder Canal Paradip Port Hooghly andDiamond Harbour populations Hap 7 was shared amongParadip Port andHooghly Feeder Canal populations Hap 24andHap 27were shared between Tapti andNarmada popula-tions Hap 1 was shared between Hooghly Feeder Canal andGanga populations Hap 15 was shared betweenHooghly andGanga populations (Table 2)

33 Population Genetic Analysis There is significant geo-graphical structuring among populations only when all pop-ulations were grouped in Bay of Bengal and Arabian SeaAMOVA revealed 442 variation among populations and9558 variation within population (Table 3) which wasfurther supported by significant 119865ST value (ie 00441 119901 lt0001) Estimates of genetic differentiation between eightpopulations using 119865 statistics are given in Table 4 The popu-lations from Narmada and Tapti showed high level of geneticdifferentiation from other populations

Tajimarsquos D was nonsignificantly negative for all exceptNarmada and Tapti populations Fursquos FS test also showednonsignificant negative values for all except Diamond Har-bour and Tapti populations Large differences were observedbetween 120579

0(population before expansion) and 120579

1(population

after expansion) In addition the SSD and Hri values werealso nonsignificant except for Diamond Harbour and Taptipopulations (Table 5) Also low haplotype diversity andmoderate to high nucleotide diversity was observed in Dia-mondHarbour andTapti populationsMismatch distributioncurves were constructed to study the genetic bottleneck andall histograms presented multimodal curves characteristic ofpopulations with constant size over time The plot betweengenetic distance and geographic distances showed a highlysignificant positive correlation indicating that geographicdistance corroborates variation in genetic distance betweenHilsa populations (1199032 = 0230 119901 le 001) (Figure 3)

4 Discussion

The significant positive correlation between geographical andgenetic distances usually referred to as ldquoisolation by distancerdquois typically suggestive of migration-drift equilibrium Ourresult showed high haplotype diversity (high genetic varia-tion) and lownucleotide diversity and significant positive FursquosFS and TajimarsquosD test except for Tapti andDiamondHarbourindicative of recent population expansion after a genetic bot-tleneck or founder events [27 28]

Hap_6Hap_37Hap_17Hap_3Hap_18Hap_36Hap_55Hap_16Hap_35Hap_33Hap_51Hap_57Hap_10Hap_34Hap_53Hap_15Hap_45Hap_32Hap_7Hap_5Hap_54Hap_11Hap_58Hap_4Hap_9Hap_44Hap_49Hap_52Hap_13Hap_2Hap_19Hap_38Hap_50Hap_56Hap_12Hap_14Hap_41Hap_1Hap_42Hap_8Hap_43Hap_39Hap_40Hap_46Hap_24Hap_47Hap_26Hap_48Hap_20Hap_30Hap_28Hap_23Hap_22Hap_25Hap_31Hap_29Hap_21Hap_27

51

53

64

79

55

58

74

5564

5569

63

71

80

6153

5381

100

6789

100 Lineage 4

Lineage 1

Lineage 3

Lineage 2

Figure 1 Molecular phylogenetic analysis of 58 haplotypes of Tilisha constructed by Maximum Likelihood method

In this study 119865 statistics (119865ST) AMOVA haplotype net-work and phylogenetic analysis revealed genetic differentia-tion among the T ilisha populations suggesting that T ilishadoes not have a single panmictic population Brahmane etal [8] separated GangaYamuna rivers stock from HooghlyandNarmada using RAPDmarkers while Brahmane et al [9]reported low genetic diversity and absence of population dif-ferentiation of Hilsa by using cytochrome b region in Ganga

Scientifica 5

Table 2 Relative haplotype frequencies in different populations of T ilisha

Haplotype Hooghly Feeder Canal Diamond Harbour Ganga Narmada Paradip Port Godavari Tapti HooghlyHap 1 0125 0 0143 0 0 0 0 0Hap 2 0125 0 0 0 0 0 0 0Hap 3 0125 0 0 0 0 0 0 0Hap 4 0125 0 0 0 0 0 0 0Hap 5 0125 0 0 0 0 0 0 0Hap 6 0125 0 0 0 0 0 0 0Hap 7 0125 0 0 0 0111 0 0 0Hap 8 0125 0375 0 0 0111 0 0 0066Hap 9 0 0125 0 0 0 0 0 0Hap 10 0 0125 0 0 0 0 0 0Hap 11 0 0125 0 0 0 0 0 0Hap 12 0 0125 0 0 0 0 0 0Hap 13 0 0125 0 0 0 0 0 0Hap 14 0 0 0143 0 0 0 0 0Hap 15 0 0 0143 0 0 0 0 0133Hap 16 0 0 0143 0 0 0 0 0Hap 17 0 0 0143 0 0 0 0 0Hap 18 0 0 0143 0 0 0 0 0Hap 19 0 0 0143 0 0 0 0 0Hap 20 0 0 0 00769 0 0 0 0Hap 21 0 0 0 00769 0 0 0 0Hap 22 0 0 0 00769 0 0 0 0Hap 23 0 0 0 00769 0 0 0 0Hap 24 0 0 0 0154 0 0 01 0Hap 25 0 0 0 00769 0 0 0 0Hap 26 0 0 0 00769 0 0 0 0Hap 27 0 0 0 00769 0 0 05 0Hap 28 0 0 0 00769 0 0 0 0Hap 29 0 0 0 00769 0 0 0 0Hap 30 0 0 0 00769 0 0 0 0Hap 31 0 0 0 00769 0 0 0 0Hap 32 0 0 0 0 0111 0 0 0Hap 33 0 0 0 0 0111 0 0 0Hap 34 0 0 0 0 0111 0 0 0Hap 35 0 0 0 0 0111 0 0 0Hap 36 0 0 0 0 0111 0 0 0Hap 37 0 0 0 0 0111 0 0 0Hap 38 0 0 0 0 0111 0 0 0Hap 39 0 0 0 0 0 0143 0 0Hap 40 0 0 0 0 0 0143 0 0Hap 41 0 0 0 0 0 0143 0 0Hap 42 0 0 0 0 0 0143 0 0Hap 43 0 0 0 0 0 0143 0 0Hap 44 0 0 0 0 0 0143 0 0Hap 45 0 0 0 0 0 0143 0 0Hap 46 0 0 0 0 0 0 02 0Hap 47 0 0 0 0 0 0 01 0Hap 48 0 0 0 0 0 0 01 0Hap 49 0 0 0 0 0 0 0 0066Hap 50 0 0 0 0 0 0 0 0066Hap 51 0 0 0 0 0 0 0 0066Hap 52 0 0 0 0 0 0 0 0066Hap 53 0 0 0 0 0 0 0 0066Hap 54 0 0 0 0 0 0 0 02Hap 55 0 0 0 0 0 0 0 0066Hap 56 0 0 0 0 0 0 0 0066Hap 57 0 0 0 0 0 0 0 0066Hap 58 0 0 0 0 0 0 0 0066

6 Scientifica

H_1

H_2

H_3

H_4

H_5

H_6

H_7H_8

H_9

H_10

H_11

H_12

H_13

H_14

H_15

H_16

H_17

H_18H_19

H_20

H_21

H_22

H_23

H_24

H_25

H_26

H_27

H_28

H_29

H_30

H_31

H_32 H_33

H_34

H_35

H_36

H_37

H_38

H_39H_40H_41

H_42

H_43

H_44

H_45 H_46

H_47

H_48

H_49H_50

H_51

H_52

H_53

H_54

H_55 H_56

H_57

H_58

mv1

mv2mv3

mv4

mv5

mv6 mv7

mv8

mv9

mv10

mv11

mv12 mv13mv14

mv15

mv16mv17

mv18

mv19

mv20

mv21

mv22

mv23mv24

mv25

mv26

Lineage 2

Lineage 3

Lineage 1

Lineage 4

Figure 2 Minimum spanning network of T ilisha based on D-loop haplotypes Haplotypes separated by single lines are one mutation apartand small circles along lines represent missing haplotypes (not sampled or extinct)

minus200minus1000 1399 2199 2999599Geographic distance

minus010

minus007

minus004

minus001

002

005

008

011

014

017

020

Gen

etic

dist

ance

Figure 3 Plot of genetic distance versus geographical distance (inKms) for analysing isolation-by-distance patterns in T ilisha

and Hooghly rivers and also felt the need of advanced geneticmarkers like ATPase 86 nicotinamide dehydrogenase sub-unit 2 (ND2) andmicrosatellite for confirmation Our resultsalso strengthen the presence of more than one stock of Hilsain Indian subcontinent however Ganga and Hooghly FeederCanal and Hooghly populations shared the same genepool

Geographical divergence and gene flow among subpopula-tions can be assessed by phylogenetic analysis and haplotypenetwork provided that gene lineages have accumulated suffi-cient polymorphisms over time and populations have beenisolated completely to allow genetic drift to act In presentstudy ML tree and haplotype network of control region haveclearly distinguished four major cladeshaplogroups namelyLineage 1 (Bay of Bengal populations Diamond HarbourGanga Hooghly Feeder Canal Godavari Paradip Port andHooghly) Lineage 2 (Godavari + some haplotypes from Bayof Bengal population) Lineage 3 (Tapti population) and Lin-eage 4 (Narmada population) Lineage 2 had mixed haplo-types mostly from Godavari and some from Hooghly FeederCanal (Hap 1) DiamondHarbour (Hap 12) Ganga (Hap 14)and common haplotype (Hap 8) this can be explained byslightlymistakenmigration routes taken by some individualsHowever formation of four clades may be explained by thephilopatry driven genetic differentiation that also corrobo-rates previous tagging studies that demonstrated limited nataland breeding dispersal ofHilsa [6]This type of behaviour hasalso been reported in highly studied American shad whichexhibits a high rate of philopatry with 97 of spawnersreturning to their natal stream [29] These results were alsoconfirmed by hierarchical AMOVA analysis that is among-group analysis (two-gene pool analysis of Ganga HooghlyHooghly Feeder Canal Diamond Harbour Paradip PortGodavari and Tapti and Narmada gene pools) having signifi-cant variation of 361 This behavioural isolation of Hilsapopulations was also strongly supported by pairwise signifi-cant 119865ST values of Tapti and Narmada with Godavari ParadipPort Diamond Harbour Hooghly Feeder Canal and Ganga

Scientifica 7

Table 3 Hierarchal analysis of molecular variance (AMOVA) for T ilisha

Source of variation df Sum of squares Variance components Percentage of variationAmong populations 7 4773 002189119881

119886442

Within populations 69 32681 047364119881119887

9558Total 76 37455 049553Fixation index 119865ST 00441

Table 4 Pairwise 119865ST (below diagonal) and associated 119901 values (above diagonal) among T ilisha populations Bold values denote statisticalsignificant values after the Bonferroni correction

Hooghly Feeder Canal Diamond Harbour Ganga Narmada Paradip Port Godavari Tapti HooghlyHooghly Feeder Canal 0 05855 09909 048649 099099 099099 003604 022523Diamond Harbour 00070 0 016216 003604 028829 019820 000000 003604Ganga minus00181 00547 0 050450 099099 099099 003604 053153Narmada 00067 00570lowast 000687 0 044144 062162 003604 003604Paradip Port minus00285 00115 0 00066 0 099099 000000 031532Godavari 0 00547 0 00068 0 0 001802 018018Tapti 0127lowast 01788lowastlowast 01302lowast 00749lowast 01243lowastlowast 01302lowast 0 000000Hooghly 00120 00462lowast 00015 00256lowast 00126 00207 01351lowastlowast 0lowastlowast119901 lt 0001 lowast119901 lt 005

Table 5 Genetic diversity indices and demographic parameters of T ilisha

Population ti tv 1205790

1205791

Tajimarsquos119863 Fursquos 119865119878 Hri SSDHooghly 26 1 7490 99999 minus007622 minus27044 004227 001401Diamond Harbour 22 1 7200 99999 minus005012 078291 016709 010294lowast

Hooghly Feeder Canal 31 6 4826 99999 minus051513 minus16082 012245 004966Paradip Port 31 6 20202 99999 minus115597 minus26925 002623 002041Godavari 22 9 2638 99999 minus080192 minus14708 009524 005183Ganga 30 6 12179 99999 minus019653 minus09430 024943 007201Narmada 37 2 17159 201765 041084 minus23072 002548 002592Tapti 27 6 12237 99999 199567 555599 013975 011588lowastlowast119901 lt 005

Although phylogenetic and phylogeographic analysis (mini-mum spanning network) shows that genetic variation is notrandomly distributed among Bay of Bengal rivers a patternof population structure and gene flow has been difficultto verify statistically Analysis of pairwise population 119865STprovides strong support for divergence betweenBay of Bengaland Arabian Sea population however biologically acceptedlevel of statistical significance (119901 le 005) is too stringent toreveal the subtle genetic differences within Bay of Bengal andArabian Sea populations when the Bonferroni correction formultiple 119901 value is applied [30]

The genetic marker most commonly used to elucidatepopulation structure in fish has been control region andwas considered sensitive enough to test for structure amongmost populations due to rapid accumulation of mutationin the control region as well as the simplicity of mtDNAinheritance through maternal line [3] However its suitabil-ity is questionable at interbasin level as true relationship

between populations was masked by high haplotype diversityand homoplasy 119865 statistics (119865ST) and AMOVA could notclearly demarcate the population structure among popu-lations from Bay of Bengal Mazumder and Alam [10]used RFLP of mitochondrial D-loop region to differen-tiate riverine estuarine and marine stocks and observedsignificant differentiation between the riverine and marine(Coxrsquos Bazar) populations but not between the marine andone of the estuarine populations as electrophoretic analysisof PCR-RFLP has lower resolving power than sequencingof the same PCR product They suggested sequencing ofD-loop region and faster evolving molecular markers forpopulation structure studies such as microsatellite lociSimilar results were recorded for yellowfin tuna whereanalysis of the control region between Atlantic and Indo-Pacific populations was unable to detect structure but clearlyresolved by PCR RFLP of the ATPase 68 and COI III genes[31]

8 Scientifica

5 Conclusion

The populations are supposed to be separated by the philo-patric behaviour Phylogeographical structure defined byAMOVA and region specific haplotypes distinguished pop-ulations up to sea level (Bay of Bengal and Arabian sea) butpopulation structuring at basin level was not noticed usingD-loop as a marker so we suggest the use of somemoderatelyevolvingmarkers such as ATPase 86 nicotinamide dehydro-genase subunit 2 (ND2) and microsatellites for elucidatingpopulation structure of Hilsa

Disclosure

Theauthors declare that all the experiments performed in thisstudy are in compliancewith the current laws of their country

Competing Interests

The authors declare that there are no competing interestsregarding the publication of this paper

Acknowledgments

The authors are thankful to the Director of National Bureauof Fish Genetic Resources for support and guidance

References

[1] S R Pillay and H Rosa Jr ldquoSynopsis of biological data on hilsaHilsa ilisha (Hamilton 1882)rdquo FAO Fisheries Biology Synopsis25 Rome Italy 1963

[2] W S Lakra V Mohindra and K K Lal ldquoFish genetics andconservation research in India status and perspectivesrdquo FishPhysiology and Biochemistry vol 33 no 4 pp 475ndash487 2007

[3] J C Avise Molecular Markers Natural History and EvolutionChapman and Hall New York NY USA 1994

[4] J Avise J Arnold R Ball et al ldquoIntraspecific phylogeographythe mitochondrial DNA bridge between population geneticsand systematicsrdquo Annual Review of Ecology Evolution andSystematics vol 18 pp 489ndash522 1987

[5] H M Bradman P M Grewe and B Appleton ldquoDirect com-parison of mitochondrial markers for the analysis of swordfishpopulation structurerdquo Fisheries Research vol 109 no 1 pp 95ndash99 2011

[6] S R Pillay K V Rao and P K Mathur ldquoPreliminary report onthe tagging of the HilsaHilsa ilisha (Hamilton)rdquo Proceedings ofthe Indo-Pacific Fisheries Council India 1962

[7] A N Ghosh R K Bhattacharya and K V Rao ldquoOn the iden-tification of the sub-populations of Hilsa ilisha (Ham) in theGangetic system with a note on their distributionrdquo Proceedingsof the National Academy of Sciences India Section B BiologicalSciences vol 34 pp 44ndash57 1968

[8] M P Brahmane M K Das M R Sinha et al ldquoUse of RAPDfingerprinting for delineating populations of hilsa shad Tenu-alosa ilisha (Hamilton 1822)rdquo Genetics and Molecular Researchvol 5 no 4 pp 643ndash652 2006

[9] M P Brahmane S N Kundu M K Das and A P SharmaldquoLowgenetic diversity and absence of population differentiationof hilsa (Tenualosa ilisha) revealed bymitochondrial DNA cyto-chrome b region in Ganga and Hooghly riversrdquo African Journalof Biotechnology vol 12 no 22 pp 3383ndash3389 2013

[10] S K Mazumder and M S Alam ldquoHigh levels of genetic vari-ability and differentiation in hilsa shad Tenualosa ilisha (Clu-peidae Clupeiformes) populations revealed byPCR-RFLP anal-ysis of the mitochondrial DNA D-loop regionrdquo Genetics andMolecular Biology vol 32 no 1 pp 190ndash196 2009

[11] K Talwar and A G Jhingran Inland Fishes of India and Adja-cent Countries Balkema Rotterdam The Netherlands 1991

[12] W Fisher and P J P Whitehead FAO Species IdentificationSheets Fishing Area 57 and 71 FAO Rome Italy 1974

[13] S M Huda andM E Haque Field Guide to Finfishes of Sundar-ban Divisional Forest Officer Bangladesh Forest DepartmentKhulna Bangladesh 2003

[14] J Sambrook and DW RussellMolecular Cloning-A LaboratoryManual Cold Spring Harbor Laboratory Cold Spring HarborNY USA 2001

[15] S Rozen andH J Skaletsky ldquoPrimer3 on theWWWfor generalusers and for biologist programmersrdquo in Bioinformatics Meth-ods and Protocols Methods inMolecular Biology S Krawetz andS Misener Eds pp 365ndash386 Humana Press Totowa NJ USA2000

[16] S F AltschulW GishWMiller EWMyers and D J LipmanldquoBasic local alignment search toolrdquo Journal ofMolecular Biologyvol 215 no 3 pp 403ndash410 1990

[17] F Sanger S Nicklen and A R Coulson ldquoDNA sequencingwith chain-terminating inhibitorsrdquo Proceedings of the NationalAcademy of Sciences of the United States of America vol 74 no12 pp 5463ndash5467 1977

[18] K Tamura D Peterson N Peterson G Stecher M Nei andS Kumar ldquoMEGA5 molecular evolutionary genetics analysisusing maximum likelihood evolutionary distance and max-imum parsimony methodsrdquo Molecular Biology and Evolutionvol 28 no 10 pp 2731ndash2739 2011

[19] L Excoffier and H E L Lischer ldquoArlequin suite ver 35 a newseries of programs to perform population genetics analysesunder Linux and Windowsrdquo Molecular Ecology Resources vol10 no 3 pp 564ndash567 2010

[20] P Librado and J Rozas ldquoDnaSP v5 a software for comprehen-sive analysis of DNA polymorphism datardquo Bioinformatics vol25 no 11 pp 1451ndash1452 2009

[21] M Kimura ldquoA simple method for estimating evolutionary ratesof base substitutions through comparative studies of nucleotidesequencesrdquo Journal ofMolecular Evolution vol 16 no 2 pp 111ndash120 1980

[22] H-J Bandelt P Forster andA Rohl ldquoMedian-joining networksfor inferring intraspecific phylogeniesrdquo Molecular Biology andEvolution vol 16 no 1 pp 37ndash48 1999

[23] S Wright ldquoThe interpretation of population structure by F-statistics with special regard to systems of matingrdquo Evolutionvol 19 no 3 pp 395ndash420 1965

[24] S W Guo and E A Thompson ldquoPerforming the exact testof Hardy-Weinberg proportion for multiple allelesrdquo Biometricsvol 48 no 2 pp 361ndash372 1992

[25] F Tajima ldquoStatistical method for testing the neutral mutationhypothesis by DNApolymorphismrdquoGenetics vol 123 no 3 pp585ndash595 1989

[26] Y-X Fu ldquoStatistical tests of neutrality of mutations againstpopulation growth hitchhiking and background selectionrdquoGenetics vol 147 no 2 pp 915ndash925 1997

[27] W S Grant and B W Bowen ldquoShallow population historiesin deep evolutionary lineages of marine fishes insights fromsardines and anchovies and lessons for conservationrdquo Journalof Heredity vol 89 no 5 pp 415ndash426 1998

Scientifica 9

[28] M A Aboim G M Menezes T Schlitt and A D RogersldquoGenetic structure and history of populations of the deep-seafish Helicolenus dactylopterus (Delaroche 1809) inferred frommtDNA sequence analysisrdquoMolecular Ecology vol 14 no 5 pp1343ndash1354 2005

[29] G D Melvin M J Dadswell and J D Martin ldquoFidelity ofAmerican shad Alosa sapidissima (Clupeidae) to its river ofprevious spawningrdquo Canadian Journal of Fisheries and AquaticSciences vol 43 no 3 pp 640ndash646 1986

[30] C A Reeb L Arcangeli and B A Block ldquoStructure andmigra-tion corridors in Pacific populations of the Swordfish Xiphiusgladius as inferred through analyses of mitochondrial DNArdquoMarine Biology vol 136 no 6 pp 1123ndash1131 2000

[31] B Ely J Vinas J R Alvarado Bremer et al ldquoConsequences ofthe historical demography on the global population structure oftwohighlymigratory cosmopolitanmarine fishesThe yellowfintuna (Thunnus albacares) and the skipjack tuna (Katsuwonuspelamis)rdquo BMC Evolutionary Biology vol 5 2005

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Anatomy Research International

PeptidesInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

International Journal of

Volume 2014

Zoology

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Molecular Biology International

GenomicsInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioinformaticsAdvances in

Marine BiologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Signal TransductionJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

Evolutionary BiologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Biochemistry Research International

ArchaeaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Genetics Research International

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Virolog y

Hindawi Publishing Corporationhttpwwwhindawicom

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Enzyme Research

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Microbiology

Scientifica 5

Table 2 Relative haplotype frequencies in different populations of T ilisha

Haplotype Hooghly Feeder Canal Diamond Harbour Ganga Narmada Paradip Port Godavari Tapti HooghlyHap 1 0125 0 0143 0 0 0 0 0Hap 2 0125 0 0 0 0 0 0 0Hap 3 0125 0 0 0 0 0 0 0Hap 4 0125 0 0 0 0 0 0 0Hap 5 0125 0 0 0 0 0 0 0Hap 6 0125 0 0 0 0 0 0 0Hap 7 0125 0 0 0 0111 0 0 0Hap 8 0125 0375 0 0 0111 0 0 0066Hap 9 0 0125 0 0 0 0 0 0Hap 10 0 0125 0 0 0 0 0 0Hap 11 0 0125 0 0 0 0 0 0Hap 12 0 0125 0 0 0 0 0 0Hap 13 0 0125 0 0 0 0 0 0Hap 14 0 0 0143 0 0 0 0 0Hap 15 0 0 0143 0 0 0 0 0133Hap 16 0 0 0143 0 0 0 0 0Hap 17 0 0 0143 0 0 0 0 0Hap 18 0 0 0143 0 0 0 0 0Hap 19 0 0 0143 0 0 0 0 0Hap 20 0 0 0 00769 0 0 0 0Hap 21 0 0 0 00769 0 0 0 0Hap 22 0 0 0 00769 0 0 0 0Hap 23 0 0 0 00769 0 0 0 0Hap 24 0 0 0 0154 0 0 01 0Hap 25 0 0 0 00769 0 0 0 0Hap 26 0 0 0 00769 0 0 0 0Hap 27 0 0 0 00769 0 0 05 0Hap 28 0 0 0 00769 0 0 0 0Hap 29 0 0 0 00769 0 0 0 0Hap 30 0 0 0 00769 0 0 0 0Hap 31 0 0 0 00769 0 0 0 0Hap 32 0 0 0 0 0111 0 0 0Hap 33 0 0 0 0 0111 0 0 0Hap 34 0 0 0 0 0111 0 0 0Hap 35 0 0 0 0 0111 0 0 0Hap 36 0 0 0 0 0111 0 0 0Hap 37 0 0 0 0 0111 0 0 0Hap 38 0 0 0 0 0111 0 0 0Hap 39 0 0 0 0 0 0143 0 0Hap 40 0 0 0 0 0 0143 0 0Hap 41 0 0 0 0 0 0143 0 0Hap 42 0 0 0 0 0 0143 0 0Hap 43 0 0 0 0 0 0143 0 0Hap 44 0 0 0 0 0 0143 0 0Hap 45 0 0 0 0 0 0143 0 0Hap 46 0 0 0 0 0 0 02 0Hap 47 0 0 0 0 0 0 01 0Hap 48 0 0 0 0 0 0 01 0Hap 49 0 0 0 0 0 0 0 0066Hap 50 0 0 0 0 0 0 0 0066Hap 51 0 0 0 0 0 0 0 0066Hap 52 0 0 0 0 0 0 0 0066Hap 53 0 0 0 0 0 0 0 0066Hap 54 0 0 0 0 0 0 0 02Hap 55 0 0 0 0 0 0 0 0066Hap 56 0 0 0 0 0 0 0 0066Hap 57 0 0 0 0 0 0 0 0066Hap 58 0 0 0 0 0 0 0 0066

6 Scientifica

H_1

H_2

H_3

H_4

H_5

H_6

H_7H_8

H_9

H_10

H_11

H_12

H_13

H_14

H_15

H_16

H_17

H_18H_19

H_20

H_21

H_22

H_23

H_24

H_25

H_26

H_27

H_28

H_29

H_30

H_31

H_32 H_33

H_34

H_35

H_36

H_37

H_38

H_39H_40H_41

H_42

H_43

H_44

H_45 H_46

H_47

H_48

H_49H_50

H_51

H_52

H_53

H_54

H_55 H_56

H_57

H_58

mv1

mv2mv3

mv4

mv5

mv6 mv7

mv8

mv9

mv10

mv11

mv12 mv13mv14

mv15

mv16mv17

mv18

mv19

mv20

mv21

mv22

mv23mv24

mv25

mv26

Lineage 2

Lineage 3

Lineage 1

Lineage 4

Figure 2 Minimum spanning network of T ilisha based on D-loop haplotypes Haplotypes separated by single lines are one mutation apartand small circles along lines represent missing haplotypes (not sampled or extinct)

minus200minus1000 1399 2199 2999599Geographic distance

minus010

minus007

minus004

minus001

002

005

008

011

014

017

020

Gen

etic

dist

ance

Figure 3 Plot of genetic distance versus geographical distance (inKms) for analysing isolation-by-distance patterns in T ilisha

and Hooghly rivers and also felt the need of advanced geneticmarkers like ATPase 86 nicotinamide dehydrogenase sub-unit 2 (ND2) andmicrosatellite for confirmation Our resultsalso strengthen the presence of more than one stock of Hilsain Indian subcontinent however Ganga and Hooghly FeederCanal and Hooghly populations shared the same genepool

Geographical divergence and gene flow among subpopula-tions can be assessed by phylogenetic analysis and haplotypenetwork provided that gene lineages have accumulated suffi-cient polymorphisms over time and populations have beenisolated completely to allow genetic drift to act In presentstudy ML tree and haplotype network of control region haveclearly distinguished four major cladeshaplogroups namelyLineage 1 (Bay of Bengal populations Diamond HarbourGanga Hooghly Feeder Canal Godavari Paradip Port andHooghly) Lineage 2 (Godavari + some haplotypes from Bayof Bengal population) Lineage 3 (Tapti population) and Lin-eage 4 (Narmada population) Lineage 2 had mixed haplo-types mostly from Godavari and some from Hooghly FeederCanal (Hap 1) DiamondHarbour (Hap 12) Ganga (Hap 14)and common haplotype (Hap 8) this can be explained byslightlymistakenmigration routes taken by some individualsHowever formation of four clades may be explained by thephilopatry driven genetic differentiation that also corrobo-rates previous tagging studies that demonstrated limited nataland breeding dispersal ofHilsa [6]This type of behaviour hasalso been reported in highly studied American shad whichexhibits a high rate of philopatry with 97 of spawnersreturning to their natal stream [29] These results were alsoconfirmed by hierarchical AMOVA analysis that is among-group analysis (two-gene pool analysis of Ganga HooghlyHooghly Feeder Canal Diamond Harbour Paradip PortGodavari and Tapti and Narmada gene pools) having signifi-cant variation of 361 This behavioural isolation of Hilsapopulations was also strongly supported by pairwise signifi-cant 119865ST values of Tapti and Narmada with Godavari ParadipPort Diamond Harbour Hooghly Feeder Canal and Ganga

Scientifica 7

Table 3 Hierarchal analysis of molecular variance (AMOVA) for T ilisha

Source of variation df Sum of squares Variance components Percentage of variationAmong populations 7 4773 002189119881

119886442

Within populations 69 32681 047364119881119887

9558Total 76 37455 049553Fixation index 119865ST 00441

Table 4 Pairwise 119865ST (below diagonal) and associated 119901 values (above diagonal) among T ilisha populations Bold values denote statisticalsignificant values after the Bonferroni correction

Hooghly Feeder Canal Diamond Harbour Ganga Narmada Paradip Port Godavari Tapti HooghlyHooghly Feeder Canal 0 05855 09909 048649 099099 099099 003604 022523Diamond Harbour 00070 0 016216 003604 028829 019820 000000 003604Ganga minus00181 00547 0 050450 099099 099099 003604 053153Narmada 00067 00570lowast 000687 0 044144 062162 003604 003604Paradip Port minus00285 00115 0 00066 0 099099 000000 031532Godavari 0 00547 0 00068 0 0 001802 018018Tapti 0127lowast 01788lowastlowast 01302lowast 00749lowast 01243lowastlowast 01302lowast 0 000000Hooghly 00120 00462lowast 00015 00256lowast 00126 00207 01351lowastlowast 0lowastlowast119901 lt 0001 lowast119901 lt 005

Table 5 Genetic diversity indices and demographic parameters of T ilisha

Population ti tv 1205790

1205791

Tajimarsquos119863 Fursquos 119865119878 Hri SSDHooghly 26 1 7490 99999 minus007622 minus27044 004227 001401Diamond Harbour 22 1 7200 99999 minus005012 078291 016709 010294lowast

Hooghly Feeder Canal 31 6 4826 99999 minus051513 minus16082 012245 004966Paradip Port 31 6 20202 99999 minus115597 minus26925 002623 002041Godavari 22 9 2638 99999 minus080192 minus14708 009524 005183Ganga 30 6 12179 99999 minus019653 minus09430 024943 007201Narmada 37 2 17159 201765 041084 minus23072 002548 002592Tapti 27 6 12237 99999 199567 555599 013975 011588lowastlowast119901 lt 005

Although phylogenetic and phylogeographic analysis (mini-mum spanning network) shows that genetic variation is notrandomly distributed among Bay of Bengal rivers a patternof population structure and gene flow has been difficultto verify statistically Analysis of pairwise population 119865STprovides strong support for divergence betweenBay of Bengaland Arabian Sea population however biologically acceptedlevel of statistical significance (119901 le 005) is too stringent toreveal the subtle genetic differences within Bay of Bengal andArabian Sea populations when the Bonferroni correction formultiple 119901 value is applied [30]

The genetic marker most commonly used to elucidatepopulation structure in fish has been control region andwas considered sensitive enough to test for structure amongmost populations due to rapid accumulation of mutationin the control region as well as the simplicity of mtDNAinheritance through maternal line [3] However its suitabil-ity is questionable at interbasin level as true relationship

between populations was masked by high haplotype diversityand homoplasy 119865 statistics (119865ST) and AMOVA could notclearly demarcate the population structure among popu-lations from Bay of Bengal Mazumder and Alam [10]used RFLP of mitochondrial D-loop region to differen-tiate riverine estuarine and marine stocks and observedsignificant differentiation between the riverine and marine(Coxrsquos Bazar) populations but not between the marine andone of the estuarine populations as electrophoretic analysisof PCR-RFLP has lower resolving power than sequencingof the same PCR product They suggested sequencing ofD-loop region and faster evolving molecular markers forpopulation structure studies such as microsatellite lociSimilar results were recorded for yellowfin tuna whereanalysis of the control region between Atlantic and Indo-Pacific populations was unable to detect structure but clearlyresolved by PCR RFLP of the ATPase 68 and COI III genes[31]

8 Scientifica

5 Conclusion

The populations are supposed to be separated by the philo-patric behaviour Phylogeographical structure defined byAMOVA and region specific haplotypes distinguished pop-ulations up to sea level (Bay of Bengal and Arabian sea) butpopulation structuring at basin level was not noticed usingD-loop as a marker so we suggest the use of somemoderatelyevolvingmarkers such as ATPase 86 nicotinamide dehydro-genase subunit 2 (ND2) and microsatellites for elucidatingpopulation structure of Hilsa

Disclosure

Theauthors declare that all the experiments performed in thisstudy are in compliancewith the current laws of their country

Competing Interests

The authors declare that there are no competing interestsregarding the publication of this paper

Acknowledgments

The authors are thankful to the Director of National Bureauof Fish Genetic Resources for support and guidance

References

[1] S R Pillay and H Rosa Jr ldquoSynopsis of biological data on hilsaHilsa ilisha (Hamilton 1882)rdquo FAO Fisheries Biology Synopsis25 Rome Italy 1963

[2] W S Lakra V Mohindra and K K Lal ldquoFish genetics andconservation research in India status and perspectivesrdquo FishPhysiology and Biochemistry vol 33 no 4 pp 475ndash487 2007

[3] J C Avise Molecular Markers Natural History and EvolutionChapman and Hall New York NY USA 1994

[4] J Avise J Arnold R Ball et al ldquoIntraspecific phylogeographythe mitochondrial DNA bridge between population geneticsand systematicsrdquo Annual Review of Ecology Evolution andSystematics vol 18 pp 489ndash522 1987

[5] H M Bradman P M Grewe and B Appleton ldquoDirect com-parison of mitochondrial markers for the analysis of swordfishpopulation structurerdquo Fisheries Research vol 109 no 1 pp 95ndash99 2011

[6] S R Pillay K V Rao and P K Mathur ldquoPreliminary report onthe tagging of the HilsaHilsa ilisha (Hamilton)rdquo Proceedings ofthe Indo-Pacific Fisheries Council India 1962

[7] A N Ghosh R K Bhattacharya and K V Rao ldquoOn the iden-tification of the sub-populations of Hilsa ilisha (Ham) in theGangetic system with a note on their distributionrdquo Proceedingsof the National Academy of Sciences India Section B BiologicalSciences vol 34 pp 44ndash57 1968

[8] M P Brahmane M K Das M R Sinha et al ldquoUse of RAPDfingerprinting for delineating populations of hilsa shad Tenu-alosa ilisha (Hamilton 1822)rdquo Genetics and Molecular Researchvol 5 no 4 pp 643ndash652 2006

[9] M P Brahmane S N Kundu M K Das and A P SharmaldquoLowgenetic diversity and absence of population differentiationof hilsa (Tenualosa ilisha) revealed bymitochondrial DNA cyto-chrome b region in Ganga and Hooghly riversrdquo African Journalof Biotechnology vol 12 no 22 pp 3383ndash3389 2013

[10] S K Mazumder and M S Alam ldquoHigh levels of genetic vari-ability and differentiation in hilsa shad Tenualosa ilisha (Clu-peidae Clupeiformes) populations revealed byPCR-RFLP anal-ysis of the mitochondrial DNA D-loop regionrdquo Genetics andMolecular Biology vol 32 no 1 pp 190ndash196 2009

[11] K Talwar and A G Jhingran Inland Fishes of India and Adja-cent Countries Balkema Rotterdam The Netherlands 1991

[12] W Fisher and P J P Whitehead FAO Species IdentificationSheets Fishing Area 57 and 71 FAO Rome Italy 1974

[13] S M Huda andM E Haque Field Guide to Finfishes of Sundar-ban Divisional Forest Officer Bangladesh Forest DepartmentKhulna Bangladesh 2003

[14] J Sambrook and DW RussellMolecular Cloning-A LaboratoryManual Cold Spring Harbor Laboratory Cold Spring HarborNY USA 2001

[15] S Rozen andH J Skaletsky ldquoPrimer3 on theWWWfor generalusers and for biologist programmersrdquo in Bioinformatics Meth-ods and Protocols Methods inMolecular Biology S Krawetz andS Misener Eds pp 365ndash386 Humana Press Totowa NJ USA2000

[16] S F AltschulW GishWMiller EWMyers and D J LipmanldquoBasic local alignment search toolrdquo Journal ofMolecular Biologyvol 215 no 3 pp 403ndash410 1990

[17] F Sanger S Nicklen and A R Coulson ldquoDNA sequencingwith chain-terminating inhibitorsrdquo Proceedings of the NationalAcademy of Sciences of the United States of America vol 74 no12 pp 5463ndash5467 1977

[18] K Tamura D Peterson N Peterson G Stecher M Nei andS Kumar ldquoMEGA5 molecular evolutionary genetics analysisusing maximum likelihood evolutionary distance and max-imum parsimony methodsrdquo Molecular Biology and Evolutionvol 28 no 10 pp 2731ndash2739 2011

[19] L Excoffier and H E L Lischer ldquoArlequin suite ver 35 a newseries of programs to perform population genetics analysesunder Linux and Windowsrdquo Molecular Ecology Resources vol10 no 3 pp 564ndash567 2010

[20] P Librado and J Rozas ldquoDnaSP v5 a software for comprehen-sive analysis of DNA polymorphism datardquo Bioinformatics vol25 no 11 pp 1451ndash1452 2009

[21] M Kimura ldquoA simple method for estimating evolutionary ratesof base substitutions through comparative studies of nucleotidesequencesrdquo Journal ofMolecular Evolution vol 16 no 2 pp 111ndash120 1980

[22] H-J Bandelt P Forster andA Rohl ldquoMedian-joining networksfor inferring intraspecific phylogeniesrdquo Molecular Biology andEvolution vol 16 no 1 pp 37ndash48 1999

[23] S Wright ldquoThe interpretation of population structure by F-statistics with special regard to systems of matingrdquo Evolutionvol 19 no 3 pp 395ndash420 1965

[24] S W Guo and E A Thompson ldquoPerforming the exact testof Hardy-Weinberg proportion for multiple allelesrdquo Biometricsvol 48 no 2 pp 361ndash372 1992

[25] F Tajima ldquoStatistical method for testing the neutral mutationhypothesis by DNApolymorphismrdquoGenetics vol 123 no 3 pp585ndash595 1989

[26] Y-X Fu ldquoStatistical tests of neutrality of mutations againstpopulation growth hitchhiking and background selectionrdquoGenetics vol 147 no 2 pp 915ndash925 1997

[27] W S Grant and B W Bowen ldquoShallow population historiesin deep evolutionary lineages of marine fishes insights fromsardines and anchovies and lessons for conservationrdquo Journalof Heredity vol 89 no 5 pp 415ndash426 1998

Scientifica 9

[28] M A Aboim G M Menezes T Schlitt and A D RogersldquoGenetic structure and history of populations of the deep-seafish Helicolenus dactylopterus (Delaroche 1809) inferred frommtDNA sequence analysisrdquoMolecular Ecology vol 14 no 5 pp1343ndash1354 2005

[29] G D Melvin M J Dadswell and J D Martin ldquoFidelity ofAmerican shad Alosa sapidissima (Clupeidae) to its river ofprevious spawningrdquo Canadian Journal of Fisheries and AquaticSciences vol 43 no 3 pp 640ndash646 1986

[30] C A Reeb L Arcangeli and B A Block ldquoStructure andmigra-tion corridors in Pacific populations of the Swordfish Xiphiusgladius as inferred through analyses of mitochondrial DNArdquoMarine Biology vol 136 no 6 pp 1123ndash1131 2000

[31] B Ely J Vinas J R Alvarado Bremer et al ldquoConsequences ofthe historical demography on the global population structure oftwohighlymigratory cosmopolitanmarine fishesThe yellowfintuna (Thunnus albacares) and the skipjack tuna (Katsuwonuspelamis)rdquo BMC Evolutionary Biology vol 5 2005

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Anatomy Research International

PeptidesInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

International Journal of

Volume 2014

Zoology

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Molecular Biology International

GenomicsInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioinformaticsAdvances in

Marine BiologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Signal TransductionJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

Evolutionary BiologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Biochemistry Research International

ArchaeaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Genetics Research International

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Virolog y

Hindawi Publishing Corporationhttpwwwhindawicom

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Enzyme Research

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Microbiology

6 Scientifica

H_1

H_2

H_3

H_4

H_5

H_6

H_7H_8

H_9

H_10

H_11

H_12

H_13

H_14

H_15

H_16

H_17

H_18H_19

H_20

H_21

H_22

H_23

H_24

H_25

H_26

H_27

H_28

H_29

H_30

H_31

H_32 H_33

H_34

H_35

H_36

H_37

H_38

H_39H_40H_41

H_42

H_43

H_44

H_45 H_46

H_47

H_48

H_49H_50

H_51

H_52

H_53

H_54

H_55 H_56

H_57

H_58

mv1

mv2mv3

mv4

mv5

mv6 mv7

mv8

mv9

mv10

mv11

mv12 mv13mv14

mv15

mv16mv17

mv18

mv19

mv20

mv21

mv22

mv23mv24

mv25

mv26

Lineage 2

Lineage 3

Lineage 1

Lineage 4

Figure 2 Minimum spanning network of T ilisha based on D-loop haplotypes Haplotypes separated by single lines are one mutation apartand small circles along lines represent missing haplotypes (not sampled or extinct)

minus200minus1000 1399 2199 2999599Geographic distance

minus010

minus007

minus004

minus001

002

005

008

011

014

017

020

Gen

etic

dist

ance

Figure 3 Plot of genetic distance versus geographical distance (inKms) for analysing isolation-by-distance patterns in T ilisha

and Hooghly rivers and also felt the need of advanced geneticmarkers like ATPase 86 nicotinamide dehydrogenase sub-unit 2 (ND2) andmicrosatellite for confirmation Our resultsalso strengthen the presence of more than one stock of Hilsain Indian subcontinent however Ganga and Hooghly FeederCanal and Hooghly populations shared the same genepool

Geographical divergence and gene flow among subpopula-tions can be assessed by phylogenetic analysis and haplotypenetwork provided that gene lineages have accumulated suffi-cient polymorphisms over time and populations have beenisolated completely to allow genetic drift to act In presentstudy ML tree and haplotype network of control region haveclearly distinguished four major cladeshaplogroups namelyLineage 1 (Bay of Bengal populations Diamond HarbourGanga Hooghly Feeder Canal Godavari Paradip Port andHooghly) Lineage 2 (Godavari + some haplotypes from Bayof Bengal population) Lineage 3 (Tapti population) and Lin-eage 4 (Narmada population) Lineage 2 had mixed haplo-types mostly from Godavari and some from Hooghly FeederCanal (Hap 1) DiamondHarbour (Hap 12) Ganga (Hap 14)and common haplotype (Hap 8) this can be explained byslightlymistakenmigration routes taken by some individualsHowever formation of four clades may be explained by thephilopatry driven genetic differentiation that also corrobo-rates previous tagging studies that demonstrated limited nataland breeding dispersal ofHilsa [6]This type of behaviour hasalso been reported in highly studied American shad whichexhibits a high rate of philopatry with 97 of spawnersreturning to their natal stream [29] These results were alsoconfirmed by hierarchical AMOVA analysis that is among-group analysis (two-gene pool analysis of Ganga HooghlyHooghly Feeder Canal Diamond Harbour Paradip PortGodavari and Tapti and Narmada gene pools) having signifi-cant variation of 361 This behavioural isolation of Hilsapopulations was also strongly supported by pairwise signifi-cant 119865ST values of Tapti and Narmada with Godavari ParadipPort Diamond Harbour Hooghly Feeder Canal and Ganga

Scientifica 7

Table 3 Hierarchal analysis of molecular variance (AMOVA) for T ilisha

Source of variation df Sum of squares Variance components Percentage of variationAmong populations 7 4773 002189119881

119886442

Within populations 69 32681 047364119881119887

9558Total 76 37455 049553Fixation index 119865ST 00441

Table 4 Pairwise 119865ST (below diagonal) and associated 119901 values (above diagonal) among T ilisha populations Bold values denote statisticalsignificant values after the Bonferroni correction

Hooghly Feeder Canal Diamond Harbour Ganga Narmada Paradip Port Godavari Tapti HooghlyHooghly Feeder Canal 0 05855 09909 048649 099099 099099 003604 022523Diamond Harbour 00070 0 016216 003604 028829 019820 000000 003604Ganga minus00181 00547 0 050450 099099 099099 003604 053153Narmada 00067 00570lowast 000687 0 044144 062162 003604 003604Paradip Port minus00285 00115 0 00066 0 099099 000000 031532Godavari 0 00547 0 00068 0 0 001802 018018Tapti 0127lowast 01788lowastlowast 01302lowast 00749lowast 01243lowastlowast 01302lowast 0 000000Hooghly 00120 00462lowast 00015 00256lowast 00126 00207 01351lowastlowast 0lowastlowast119901 lt 0001 lowast119901 lt 005

Table 5 Genetic diversity indices and demographic parameters of T ilisha

Population ti tv 1205790

1205791

Tajimarsquos119863 Fursquos 119865119878 Hri SSDHooghly 26 1 7490 99999 minus007622 minus27044 004227 001401Diamond Harbour 22 1 7200 99999 minus005012 078291 016709 010294lowast

Hooghly Feeder Canal 31 6 4826 99999 minus051513 minus16082 012245 004966Paradip Port 31 6 20202 99999 minus115597 minus26925 002623 002041Godavari 22 9 2638 99999 minus080192 minus14708 009524 005183Ganga 30 6 12179 99999 minus019653 minus09430 024943 007201Narmada 37 2 17159 201765 041084 minus23072 002548 002592Tapti 27 6 12237 99999 199567 555599 013975 011588lowastlowast119901 lt 005

Although phylogenetic and phylogeographic analysis (mini-mum spanning network) shows that genetic variation is notrandomly distributed among Bay of Bengal rivers a patternof population structure and gene flow has been difficultto verify statistically Analysis of pairwise population 119865STprovides strong support for divergence betweenBay of Bengaland Arabian Sea population however biologically acceptedlevel of statistical significance (119901 le 005) is too stringent toreveal the subtle genetic differences within Bay of Bengal andArabian Sea populations when the Bonferroni correction formultiple 119901 value is applied [30]

The genetic marker most commonly used to elucidatepopulation structure in fish has been control region andwas considered sensitive enough to test for structure amongmost populations due to rapid accumulation of mutationin the control region as well as the simplicity of mtDNAinheritance through maternal line [3] However its suitabil-ity is questionable at interbasin level as true relationship

between populations was masked by high haplotype diversityand homoplasy 119865 statistics (119865ST) and AMOVA could notclearly demarcate the population structure among popu-lations from Bay of Bengal Mazumder and Alam [10]used RFLP of mitochondrial D-loop region to differen-tiate riverine estuarine and marine stocks and observedsignificant differentiation between the riverine and marine(Coxrsquos Bazar) populations but not between the marine andone of the estuarine populations as electrophoretic analysisof PCR-RFLP has lower resolving power than sequencingof the same PCR product They suggested sequencing ofD-loop region and faster evolving molecular markers forpopulation structure studies such as microsatellite lociSimilar results were recorded for yellowfin tuna whereanalysis of the control region between Atlantic and Indo-Pacific populations was unable to detect structure but clearlyresolved by PCR RFLP of the ATPase 68 and COI III genes[31]

8 Scientifica

5 Conclusion

The populations are supposed to be separated by the philo-patric behaviour Phylogeographical structure defined byAMOVA and region specific haplotypes distinguished pop-ulations up to sea level (Bay of Bengal and Arabian sea) butpopulation structuring at basin level was not noticed usingD-loop as a marker so we suggest the use of somemoderatelyevolvingmarkers such as ATPase 86 nicotinamide dehydro-genase subunit 2 (ND2) and microsatellites for elucidatingpopulation structure of Hilsa

Disclosure

Theauthors declare that all the experiments performed in thisstudy are in compliancewith the current laws of their country

Competing Interests

The authors declare that there are no competing interestsregarding the publication of this paper

Acknowledgments

The authors are thankful to the Director of National Bureauof Fish Genetic Resources for support and guidance

References

[1] S R Pillay and H Rosa Jr ldquoSynopsis of biological data on hilsaHilsa ilisha (Hamilton 1882)rdquo FAO Fisheries Biology Synopsis25 Rome Italy 1963

[2] W S Lakra V Mohindra and K K Lal ldquoFish genetics andconservation research in India status and perspectivesrdquo FishPhysiology and Biochemistry vol 33 no 4 pp 475ndash487 2007

[3] J C Avise Molecular Markers Natural History and EvolutionChapman and Hall New York NY USA 1994

[4] J Avise J Arnold R Ball et al ldquoIntraspecific phylogeographythe mitochondrial DNA bridge between population geneticsand systematicsrdquo Annual Review of Ecology Evolution andSystematics vol 18 pp 489ndash522 1987

[5] H M Bradman P M Grewe and B Appleton ldquoDirect com-parison of mitochondrial markers for the analysis of swordfishpopulation structurerdquo Fisheries Research vol 109 no 1 pp 95ndash99 2011

[6] S R Pillay K V Rao and P K Mathur ldquoPreliminary report onthe tagging of the HilsaHilsa ilisha (Hamilton)rdquo Proceedings ofthe Indo-Pacific Fisheries Council India 1962

[7] A N Ghosh R K Bhattacharya and K V Rao ldquoOn the iden-tification of the sub-populations of Hilsa ilisha (Ham) in theGangetic system with a note on their distributionrdquo Proceedingsof the National Academy of Sciences India Section B BiologicalSciences vol 34 pp 44ndash57 1968

[8] M P Brahmane M K Das M R Sinha et al ldquoUse of RAPDfingerprinting for delineating populations of hilsa shad Tenu-alosa ilisha (Hamilton 1822)rdquo Genetics and Molecular Researchvol 5 no 4 pp 643ndash652 2006

[9] M P Brahmane S N Kundu M K Das and A P SharmaldquoLowgenetic diversity and absence of population differentiationof hilsa (Tenualosa ilisha) revealed bymitochondrial DNA cyto-chrome b region in Ganga and Hooghly riversrdquo African Journalof Biotechnology vol 12 no 22 pp 3383ndash3389 2013

[10] S K Mazumder and M S Alam ldquoHigh levels of genetic vari-ability and differentiation in hilsa shad Tenualosa ilisha (Clu-peidae Clupeiformes) populations revealed byPCR-RFLP anal-ysis of the mitochondrial DNA D-loop regionrdquo Genetics andMolecular Biology vol 32 no 1 pp 190ndash196 2009

[11] K Talwar and A G Jhingran Inland Fishes of India and Adja-cent Countries Balkema Rotterdam The Netherlands 1991

[12] W Fisher and P J P Whitehead FAO Species IdentificationSheets Fishing Area 57 and 71 FAO Rome Italy 1974

[13] S M Huda andM E Haque Field Guide to Finfishes of Sundar-ban Divisional Forest Officer Bangladesh Forest DepartmentKhulna Bangladesh 2003

[14] J Sambrook and DW RussellMolecular Cloning-A LaboratoryManual Cold Spring Harbor Laboratory Cold Spring HarborNY USA 2001

[15] S Rozen andH J Skaletsky ldquoPrimer3 on theWWWfor generalusers and for biologist programmersrdquo in Bioinformatics Meth-ods and Protocols Methods inMolecular Biology S Krawetz andS Misener Eds pp 365ndash386 Humana Press Totowa NJ USA2000

[16] S F AltschulW GishWMiller EWMyers and D J LipmanldquoBasic local alignment search toolrdquo Journal ofMolecular Biologyvol 215 no 3 pp 403ndash410 1990

[17] F Sanger S Nicklen and A R Coulson ldquoDNA sequencingwith chain-terminating inhibitorsrdquo Proceedings of the NationalAcademy of Sciences of the United States of America vol 74 no12 pp 5463ndash5467 1977

[18] K Tamura D Peterson N Peterson G Stecher M Nei andS Kumar ldquoMEGA5 molecular evolutionary genetics analysisusing maximum likelihood evolutionary distance and max-imum parsimony methodsrdquo Molecular Biology and Evolutionvol 28 no 10 pp 2731ndash2739 2011

[19] L Excoffier and H E L Lischer ldquoArlequin suite ver 35 a newseries of programs to perform population genetics analysesunder Linux and Windowsrdquo Molecular Ecology Resources vol10 no 3 pp 564ndash567 2010

[20] P Librado and J Rozas ldquoDnaSP v5 a software for comprehen-sive analysis of DNA polymorphism datardquo Bioinformatics vol25 no 11 pp 1451ndash1452 2009

[21] M Kimura ldquoA simple method for estimating evolutionary ratesof base substitutions through comparative studies of nucleotidesequencesrdquo Journal ofMolecular Evolution vol 16 no 2 pp 111ndash120 1980

[22] H-J Bandelt P Forster andA Rohl ldquoMedian-joining networksfor inferring intraspecific phylogeniesrdquo Molecular Biology andEvolution vol 16 no 1 pp 37ndash48 1999

[23] S Wright ldquoThe interpretation of population structure by F-statistics with special regard to systems of matingrdquo Evolutionvol 19 no 3 pp 395ndash420 1965

[24] S W Guo and E A Thompson ldquoPerforming the exact testof Hardy-Weinberg proportion for multiple allelesrdquo Biometricsvol 48 no 2 pp 361ndash372 1992

[25] F Tajima ldquoStatistical method for testing the neutral mutationhypothesis by DNApolymorphismrdquoGenetics vol 123 no 3 pp585ndash595 1989

[26] Y-X Fu ldquoStatistical tests of neutrality of mutations againstpopulation growth hitchhiking and background selectionrdquoGenetics vol 147 no 2 pp 915ndash925 1997

[27] W S Grant and B W Bowen ldquoShallow population historiesin deep evolutionary lineages of marine fishes insights fromsardines and anchovies and lessons for conservationrdquo Journalof Heredity vol 89 no 5 pp 415ndash426 1998

Scientifica 9

[28] M A Aboim G M Menezes T Schlitt and A D RogersldquoGenetic structure and history of populations of the deep-seafish Helicolenus dactylopterus (Delaroche 1809) inferred frommtDNA sequence analysisrdquoMolecular Ecology vol 14 no 5 pp1343ndash1354 2005

[29] G D Melvin M J Dadswell and J D Martin ldquoFidelity ofAmerican shad Alosa sapidissima (Clupeidae) to its river ofprevious spawningrdquo Canadian Journal of Fisheries and AquaticSciences vol 43 no 3 pp 640ndash646 1986

[30] C A Reeb L Arcangeli and B A Block ldquoStructure andmigra-tion corridors in Pacific populations of the Swordfish Xiphiusgladius as inferred through analyses of mitochondrial DNArdquoMarine Biology vol 136 no 6 pp 1123ndash1131 2000

[31] B Ely J Vinas J R Alvarado Bremer et al ldquoConsequences ofthe historical demography on the global population structure oftwohighlymigratory cosmopolitanmarine fishesThe yellowfintuna (Thunnus albacares) and the skipjack tuna (Katsuwonuspelamis)rdquo BMC Evolutionary Biology vol 5 2005

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Anatomy Research International

PeptidesInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

International Journal of

Volume 2014

Zoology

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Molecular Biology International

GenomicsInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioinformaticsAdvances in

Marine BiologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Signal TransductionJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

Evolutionary BiologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Biochemistry Research International

ArchaeaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Genetics Research International

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Virolog y

Hindawi Publishing Corporationhttpwwwhindawicom

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Enzyme Research

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Microbiology

Scientifica 7

Table 3 Hierarchal analysis of molecular variance (AMOVA) for T ilisha

Source of variation df Sum of squares Variance components Percentage of variationAmong populations 7 4773 002189119881

119886442

Within populations 69 32681 047364119881119887

9558Total 76 37455 049553Fixation index 119865ST 00441

Table 4 Pairwise 119865ST (below diagonal) and associated 119901 values (above diagonal) among T ilisha populations Bold values denote statisticalsignificant values after the Bonferroni correction

Hooghly Feeder Canal Diamond Harbour Ganga Narmada Paradip Port Godavari Tapti HooghlyHooghly Feeder Canal 0 05855 09909 048649 099099 099099 003604 022523Diamond Harbour 00070 0 016216 003604 028829 019820 000000 003604Ganga minus00181 00547 0 050450 099099 099099 003604 053153Narmada 00067 00570lowast 000687 0 044144 062162 003604 003604Paradip Port minus00285 00115 0 00066 0 099099 000000 031532Godavari 0 00547 0 00068 0 0 001802 018018Tapti 0127lowast 01788lowastlowast 01302lowast 00749lowast 01243lowastlowast 01302lowast 0 000000Hooghly 00120 00462lowast 00015 00256lowast 00126 00207 01351lowastlowast 0lowastlowast119901 lt 0001 lowast119901 lt 005

Table 5 Genetic diversity indices and demographic parameters of T ilisha

Population ti tv 1205790

1205791

Tajimarsquos119863 Fursquos 119865119878 Hri SSDHooghly 26 1 7490 99999 minus007622 minus27044 004227 001401Diamond Harbour 22 1 7200 99999 minus005012 078291 016709 010294lowast

Hooghly Feeder Canal 31 6 4826 99999 minus051513 minus16082 012245 004966Paradip Port 31 6 20202 99999 minus115597 minus26925 002623 002041Godavari 22 9 2638 99999 minus080192 minus14708 009524 005183Ganga 30 6 12179 99999 minus019653 minus09430 024943 007201Narmada 37 2 17159 201765 041084 minus23072 002548 002592Tapti 27 6 12237 99999 199567 555599 013975 011588lowastlowast119901 lt 005

Although phylogenetic and phylogeographic analysis (mini-mum spanning network) shows that genetic variation is notrandomly distributed among Bay of Bengal rivers a patternof population structure and gene flow has been difficultto verify statistically Analysis of pairwise population 119865STprovides strong support for divergence betweenBay of Bengaland Arabian Sea population however biologically acceptedlevel of statistical significance (119901 le 005) is too stringent toreveal the subtle genetic differences within Bay of Bengal andArabian Sea populations when the Bonferroni correction formultiple 119901 value is applied [30]

The genetic marker most commonly used to elucidatepopulation structure in fish has been control region andwas considered sensitive enough to test for structure amongmost populations due to rapid accumulation of mutationin the control region as well as the simplicity of mtDNAinheritance through maternal line [3] However its suitabil-ity is questionable at interbasin level as true relationship

between populations was masked by high haplotype diversityand homoplasy 119865 statistics (119865ST) and AMOVA could notclearly demarcate the population structure among popu-lations from Bay of Bengal Mazumder and Alam [10]used RFLP of mitochondrial D-loop region to differen-tiate riverine estuarine and marine stocks and observedsignificant differentiation between the riverine and marine(Coxrsquos Bazar) populations but not between the marine andone of the estuarine populations as electrophoretic analysisof PCR-RFLP has lower resolving power than sequencingof the same PCR product They suggested sequencing ofD-loop region and faster evolving molecular markers forpopulation structure studies such as microsatellite lociSimilar results were recorded for yellowfin tuna whereanalysis of the control region between Atlantic and Indo-Pacific populations was unable to detect structure but clearlyresolved by PCR RFLP of the ATPase 68 and COI III genes[31]

8 Scientifica

5 Conclusion

The populations are supposed to be separated by the philo-patric behaviour Phylogeographical structure defined byAMOVA and region specific haplotypes distinguished pop-ulations up to sea level (Bay of Bengal and Arabian sea) butpopulation structuring at basin level was not noticed usingD-loop as a marker so we suggest the use of somemoderatelyevolvingmarkers such as ATPase 86 nicotinamide dehydro-genase subunit 2 (ND2) and microsatellites for elucidatingpopulation structure of Hilsa

Disclosure

Theauthors declare that all the experiments performed in thisstudy are in compliancewith the current laws of their country

Competing Interests

The authors declare that there are no competing interestsregarding the publication of this paper

Acknowledgments

The authors are thankful to the Director of National Bureauof Fish Genetic Resources for support and guidance

References

[1] S R Pillay and H Rosa Jr ldquoSynopsis of biological data on hilsaHilsa ilisha (Hamilton 1882)rdquo FAO Fisheries Biology Synopsis25 Rome Italy 1963

[2] W S Lakra V Mohindra and K K Lal ldquoFish genetics andconservation research in India status and perspectivesrdquo FishPhysiology and Biochemistry vol 33 no 4 pp 475ndash487 2007

[3] J C Avise Molecular Markers Natural History and EvolutionChapman and Hall New York NY USA 1994

[4] J Avise J Arnold R Ball et al ldquoIntraspecific phylogeographythe mitochondrial DNA bridge between population geneticsand systematicsrdquo Annual Review of Ecology Evolution andSystematics vol 18 pp 489ndash522 1987

[5] H M Bradman P M Grewe and B Appleton ldquoDirect com-parison of mitochondrial markers for the analysis of swordfishpopulation structurerdquo Fisheries Research vol 109 no 1 pp 95ndash99 2011

[6] S R Pillay K V Rao and P K Mathur ldquoPreliminary report onthe tagging of the HilsaHilsa ilisha (Hamilton)rdquo Proceedings ofthe Indo-Pacific Fisheries Council India 1962

[7] A N Ghosh R K Bhattacharya and K V Rao ldquoOn the iden-tification of the sub-populations of Hilsa ilisha (Ham) in theGangetic system with a note on their distributionrdquo Proceedingsof the National Academy of Sciences India Section B BiologicalSciences vol 34 pp 44ndash57 1968

[8] M P Brahmane M K Das M R Sinha et al ldquoUse of RAPDfingerprinting for delineating populations of hilsa shad Tenu-alosa ilisha (Hamilton 1822)rdquo Genetics and Molecular Researchvol 5 no 4 pp 643ndash652 2006

[9] M P Brahmane S N Kundu M K Das and A P SharmaldquoLowgenetic diversity and absence of population differentiationof hilsa (Tenualosa ilisha) revealed bymitochondrial DNA cyto-chrome b region in Ganga and Hooghly riversrdquo African Journalof Biotechnology vol 12 no 22 pp 3383ndash3389 2013

[10] S K Mazumder and M S Alam ldquoHigh levels of genetic vari-ability and differentiation in hilsa shad Tenualosa ilisha (Clu-peidae Clupeiformes) populations revealed byPCR-RFLP anal-ysis of the mitochondrial DNA D-loop regionrdquo Genetics andMolecular Biology vol 32 no 1 pp 190ndash196 2009

[11] K Talwar and A G Jhingran Inland Fishes of India and Adja-cent Countries Balkema Rotterdam The Netherlands 1991

[12] W Fisher and P J P Whitehead FAO Species IdentificationSheets Fishing Area 57 and 71 FAO Rome Italy 1974

[13] S M Huda andM E Haque Field Guide to Finfishes of Sundar-ban Divisional Forest Officer Bangladesh Forest DepartmentKhulna Bangladesh 2003

[14] J Sambrook and DW RussellMolecular Cloning-A LaboratoryManual Cold Spring Harbor Laboratory Cold Spring HarborNY USA 2001

[15] S Rozen andH J Skaletsky ldquoPrimer3 on theWWWfor generalusers and for biologist programmersrdquo in Bioinformatics Meth-ods and Protocols Methods inMolecular Biology S Krawetz andS Misener Eds pp 365ndash386 Humana Press Totowa NJ USA2000

[16] S F AltschulW GishWMiller EWMyers and D J LipmanldquoBasic local alignment search toolrdquo Journal ofMolecular Biologyvol 215 no 3 pp 403ndash410 1990

[17] F Sanger S Nicklen and A R Coulson ldquoDNA sequencingwith chain-terminating inhibitorsrdquo Proceedings of the NationalAcademy of Sciences of the United States of America vol 74 no12 pp 5463ndash5467 1977

[18] K Tamura D Peterson N Peterson G Stecher M Nei andS Kumar ldquoMEGA5 molecular evolutionary genetics analysisusing maximum likelihood evolutionary distance and max-imum parsimony methodsrdquo Molecular Biology and Evolutionvol 28 no 10 pp 2731ndash2739 2011

[19] L Excoffier and H E L Lischer ldquoArlequin suite ver 35 a newseries of programs to perform population genetics analysesunder Linux and Windowsrdquo Molecular Ecology Resources vol10 no 3 pp 564ndash567 2010

[20] P Librado and J Rozas ldquoDnaSP v5 a software for comprehen-sive analysis of DNA polymorphism datardquo Bioinformatics vol25 no 11 pp 1451ndash1452 2009

[21] M Kimura ldquoA simple method for estimating evolutionary ratesof base substitutions through comparative studies of nucleotidesequencesrdquo Journal ofMolecular Evolution vol 16 no 2 pp 111ndash120 1980

[22] H-J Bandelt P Forster andA Rohl ldquoMedian-joining networksfor inferring intraspecific phylogeniesrdquo Molecular Biology andEvolution vol 16 no 1 pp 37ndash48 1999

[23] S Wright ldquoThe interpretation of population structure by F-statistics with special regard to systems of matingrdquo Evolutionvol 19 no 3 pp 395ndash420 1965

[24] S W Guo and E A Thompson ldquoPerforming the exact testof Hardy-Weinberg proportion for multiple allelesrdquo Biometricsvol 48 no 2 pp 361ndash372 1992

[25] F Tajima ldquoStatistical method for testing the neutral mutationhypothesis by DNApolymorphismrdquoGenetics vol 123 no 3 pp585ndash595 1989

[26] Y-X Fu ldquoStatistical tests of neutrality of mutations againstpopulation growth hitchhiking and background selectionrdquoGenetics vol 147 no 2 pp 915ndash925 1997

[27] W S Grant and B W Bowen ldquoShallow population historiesin deep evolutionary lineages of marine fishes insights fromsardines and anchovies and lessons for conservationrdquo Journalof Heredity vol 89 no 5 pp 415ndash426 1998

Scientifica 9

[28] M A Aboim G M Menezes T Schlitt and A D RogersldquoGenetic structure and history of populations of the deep-seafish Helicolenus dactylopterus (Delaroche 1809) inferred frommtDNA sequence analysisrdquoMolecular Ecology vol 14 no 5 pp1343ndash1354 2005

[29] G D Melvin M J Dadswell and J D Martin ldquoFidelity ofAmerican shad Alosa sapidissima (Clupeidae) to its river ofprevious spawningrdquo Canadian Journal of Fisheries and AquaticSciences vol 43 no 3 pp 640ndash646 1986

[30] C A Reeb L Arcangeli and B A Block ldquoStructure andmigra-tion corridors in Pacific populations of the Swordfish Xiphiusgladius as inferred through analyses of mitochondrial DNArdquoMarine Biology vol 136 no 6 pp 1123ndash1131 2000

[31] B Ely J Vinas J R Alvarado Bremer et al ldquoConsequences ofthe historical demography on the global population structure oftwohighlymigratory cosmopolitanmarine fishesThe yellowfintuna (Thunnus albacares) and the skipjack tuna (Katsuwonuspelamis)rdquo BMC Evolutionary Biology vol 5 2005

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Anatomy Research International

PeptidesInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

International Journal of

Volume 2014

Zoology

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Molecular Biology International

GenomicsInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioinformaticsAdvances in

Marine BiologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Signal TransductionJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

Evolutionary BiologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Biochemistry Research International

ArchaeaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Genetics Research International

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Virolog y

Hindawi Publishing Corporationhttpwwwhindawicom

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Enzyme Research

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Microbiology

8 Scientifica

5 Conclusion

The populations are supposed to be separated by the philo-patric behaviour Phylogeographical structure defined byAMOVA and region specific haplotypes distinguished pop-ulations up to sea level (Bay of Bengal and Arabian sea) butpopulation structuring at basin level was not noticed usingD-loop as a marker so we suggest the use of somemoderatelyevolvingmarkers such as ATPase 86 nicotinamide dehydro-genase subunit 2 (ND2) and microsatellites for elucidatingpopulation structure of Hilsa

Disclosure

Theauthors declare that all the experiments performed in thisstudy are in compliancewith the current laws of their country

Competing Interests

The authors declare that there are no competing interestsregarding the publication of this paper

Acknowledgments

The authors are thankful to the Director of National Bureauof Fish Genetic Resources for support and guidance

References

[1] S R Pillay and H Rosa Jr ldquoSynopsis of biological data on hilsaHilsa ilisha (Hamilton 1882)rdquo FAO Fisheries Biology Synopsis25 Rome Italy 1963

[2] W S Lakra V Mohindra and K K Lal ldquoFish genetics andconservation research in India status and perspectivesrdquo FishPhysiology and Biochemistry vol 33 no 4 pp 475ndash487 2007

[3] J C Avise Molecular Markers Natural History and EvolutionChapman and Hall New York NY USA 1994

[4] J Avise J Arnold R Ball et al ldquoIntraspecific phylogeographythe mitochondrial DNA bridge between population geneticsand systematicsrdquo Annual Review of Ecology Evolution andSystematics vol 18 pp 489ndash522 1987

[5] H M Bradman P M Grewe and B Appleton ldquoDirect com-parison of mitochondrial markers for the analysis of swordfishpopulation structurerdquo Fisheries Research vol 109 no 1 pp 95ndash99 2011

[6] S R Pillay K V Rao and P K Mathur ldquoPreliminary report onthe tagging of the HilsaHilsa ilisha (Hamilton)rdquo Proceedings ofthe Indo-Pacific Fisheries Council India 1962

[7] A N Ghosh R K Bhattacharya and K V Rao ldquoOn the iden-tification of the sub-populations of Hilsa ilisha (Ham) in theGangetic system with a note on their distributionrdquo Proceedingsof the National Academy of Sciences India Section B BiologicalSciences vol 34 pp 44ndash57 1968

[8] M P Brahmane M K Das M R Sinha et al ldquoUse of RAPDfingerprinting for delineating populations of hilsa shad Tenu-alosa ilisha (Hamilton 1822)rdquo Genetics and Molecular Researchvol 5 no 4 pp 643ndash652 2006

[9] M P Brahmane S N Kundu M K Das and A P SharmaldquoLowgenetic diversity and absence of population differentiationof hilsa (Tenualosa ilisha) revealed bymitochondrial DNA cyto-chrome b region in Ganga and Hooghly riversrdquo African Journalof Biotechnology vol 12 no 22 pp 3383ndash3389 2013

[10] S K Mazumder and M S Alam ldquoHigh levels of genetic vari-ability and differentiation in hilsa shad Tenualosa ilisha (Clu-peidae Clupeiformes) populations revealed byPCR-RFLP anal-ysis of the mitochondrial DNA D-loop regionrdquo Genetics andMolecular Biology vol 32 no 1 pp 190ndash196 2009

[11] K Talwar and A G Jhingran Inland Fishes of India and Adja-cent Countries Balkema Rotterdam The Netherlands 1991

[12] W Fisher and P J P Whitehead FAO Species IdentificationSheets Fishing Area 57 and 71 FAO Rome Italy 1974

[13] S M Huda andM E Haque Field Guide to Finfishes of Sundar-ban Divisional Forest Officer Bangladesh Forest DepartmentKhulna Bangladesh 2003

[14] J Sambrook and DW RussellMolecular Cloning-A LaboratoryManual Cold Spring Harbor Laboratory Cold Spring HarborNY USA 2001

[15] S Rozen andH J Skaletsky ldquoPrimer3 on theWWWfor generalusers and for biologist programmersrdquo in Bioinformatics Meth-ods and Protocols Methods inMolecular Biology S Krawetz andS Misener Eds pp 365ndash386 Humana Press Totowa NJ USA2000

[16] S F AltschulW GishWMiller EWMyers and D J LipmanldquoBasic local alignment search toolrdquo Journal ofMolecular Biologyvol 215 no 3 pp 403ndash410 1990

[17] F Sanger S Nicklen and A R Coulson ldquoDNA sequencingwith chain-terminating inhibitorsrdquo Proceedings of the NationalAcademy of Sciences of the United States of America vol 74 no12 pp 5463ndash5467 1977

[18] K Tamura D Peterson N Peterson G Stecher M Nei andS Kumar ldquoMEGA5 molecular evolutionary genetics analysisusing maximum likelihood evolutionary distance and max-imum parsimony methodsrdquo Molecular Biology and Evolutionvol 28 no 10 pp 2731ndash2739 2011

[19] L Excoffier and H E L Lischer ldquoArlequin suite ver 35 a newseries of programs to perform population genetics analysesunder Linux and Windowsrdquo Molecular Ecology Resources vol10 no 3 pp 564ndash567 2010

[20] P Librado and J Rozas ldquoDnaSP v5 a software for comprehen-sive analysis of DNA polymorphism datardquo Bioinformatics vol25 no 11 pp 1451ndash1452 2009

[21] M Kimura ldquoA simple method for estimating evolutionary ratesof base substitutions through comparative studies of nucleotidesequencesrdquo Journal ofMolecular Evolution vol 16 no 2 pp 111ndash120 1980

[22] H-J Bandelt P Forster andA Rohl ldquoMedian-joining networksfor inferring intraspecific phylogeniesrdquo Molecular Biology andEvolution vol 16 no 1 pp 37ndash48 1999

[23] S Wright ldquoThe interpretation of population structure by F-statistics with special regard to systems of matingrdquo Evolutionvol 19 no 3 pp 395ndash420 1965

[24] S W Guo and E A Thompson ldquoPerforming the exact testof Hardy-Weinberg proportion for multiple allelesrdquo Biometricsvol 48 no 2 pp 361ndash372 1992

[25] F Tajima ldquoStatistical method for testing the neutral mutationhypothesis by DNApolymorphismrdquoGenetics vol 123 no 3 pp585ndash595 1989

[26] Y-X Fu ldquoStatistical tests of neutrality of mutations againstpopulation growth hitchhiking and background selectionrdquoGenetics vol 147 no 2 pp 915ndash925 1997

[27] W S Grant and B W Bowen ldquoShallow population historiesin deep evolutionary lineages of marine fishes insights fromsardines and anchovies and lessons for conservationrdquo Journalof Heredity vol 89 no 5 pp 415ndash426 1998

Scientifica 9

[28] M A Aboim G M Menezes T Schlitt and A D RogersldquoGenetic structure and history of populations of the deep-seafish Helicolenus dactylopterus (Delaroche 1809) inferred frommtDNA sequence analysisrdquoMolecular Ecology vol 14 no 5 pp1343ndash1354 2005

[29] G D Melvin M J Dadswell and J D Martin ldquoFidelity ofAmerican shad Alosa sapidissima (Clupeidae) to its river ofprevious spawningrdquo Canadian Journal of Fisheries and AquaticSciences vol 43 no 3 pp 640ndash646 1986

[30] C A Reeb L Arcangeli and B A Block ldquoStructure andmigra-tion corridors in Pacific populations of the Swordfish Xiphiusgladius as inferred through analyses of mitochondrial DNArdquoMarine Biology vol 136 no 6 pp 1123ndash1131 2000

[31] B Ely J Vinas J R Alvarado Bremer et al ldquoConsequences ofthe historical demography on the global population structure oftwohighlymigratory cosmopolitanmarine fishesThe yellowfintuna (Thunnus albacares) and the skipjack tuna (Katsuwonuspelamis)rdquo BMC Evolutionary Biology vol 5 2005

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Anatomy Research International

PeptidesInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

International Journal of

Volume 2014

Zoology

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Molecular Biology International

GenomicsInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioinformaticsAdvances in

Marine BiologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Signal TransductionJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

Evolutionary BiologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Biochemistry Research International

ArchaeaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Genetics Research International

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Virolog y

Hindawi Publishing Corporationhttpwwwhindawicom

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Enzyme Research

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Microbiology

Scientifica 9

[28] M A Aboim G M Menezes T Schlitt and A D RogersldquoGenetic structure and history of populations of the deep-seafish Helicolenus dactylopterus (Delaroche 1809) inferred frommtDNA sequence analysisrdquoMolecular Ecology vol 14 no 5 pp1343ndash1354 2005

[29] G D Melvin M J Dadswell and J D Martin ldquoFidelity ofAmerican shad Alosa sapidissima (Clupeidae) to its river ofprevious spawningrdquo Canadian Journal of Fisheries and AquaticSciences vol 43 no 3 pp 640ndash646 1986

[30] C A Reeb L Arcangeli and B A Block ldquoStructure andmigra-tion corridors in Pacific populations of the Swordfish Xiphiusgladius as inferred through analyses of mitochondrial DNArdquoMarine Biology vol 136 no 6 pp 1123ndash1131 2000

[31] B Ely J Vinas J R Alvarado Bremer et al ldquoConsequences ofthe historical demography on the global population structure oftwohighlymigratory cosmopolitanmarine fishesThe yellowfintuna (Thunnus albacares) and the skipjack tuna (Katsuwonuspelamis)rdquo BMC Evolutionary Biology vol 5 2005

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Anatomy Research International

PeptidesInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

International Journal of

Volume 2014

Zoology

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Molecular Biology International

GenomicsInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioinformaticsAdvances in

Marine BiologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Signal TransductionJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

Evolutionary BiologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Biochemistry Research International

ArchaeaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Genetics Research International

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Virolog y

Hindawi Publishing Corporationhttpwwwhindawicom

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Enzyme Research

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Microbiology

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Anatomy Research International

PeptidesInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

International Journal of

Volume 2014

Zoology

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Molecular Biology International

GenomicsInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioinformaticsAdvances in

Marine BiologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Signal TransductionJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

Evolutionary BiologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Biochemistry Research International

ArchaeaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Genetics Research International

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Virolog y

Hindawi Publishing Corporationhttpwwwhindawicom

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Enzyme Research

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Microbiology