Aquaculture Research, 2009, 40, 1111^1122
doi:10.1111/j.1365-2109.2009.02206.x
Temporal genetic heterogeneity of juvenile orangespotted grouper (Epinephelus coioides, Pisces: Serranidae) Panuwat Pumitinsee1,Wansuk Senanan1, Uthairat Na-Nakorn2,Wongpathom Kamonrat3 & Worawut Koedprang4 1
Department of Aquatic Science, Faculty of Science, Burapha University, Chon Buri,Thailand
2
Department of Aquaculture, Faculty of Fisheries, Kasetsart University, Bangkok,Thailand
3
Department of Fisheries, Ministry of Agriculture and Cooperatives, Chatuchak, Bangkok,Thailand
4
Department of Aquaculture, Faculty of Science and Fisheries Technology, Rajamangala University of Technology Srivijaya,
Trang,Thailand Correspondence:Wansuk Senanan, Department of Aquatic Science, Faculty of Science, Burapha University, Chon Buri 20131,Thailand. E-mail:
[email protected]
Abstract Juveniles of orange-spotted grouper (Epinephelus coioides), a tropical serranid species, are heavily harvested for aquaculture seeds from nursing grounds in several Southeast Asian countries. Because juveniles of similar sizes are present in a nursery area throughout the year, we aimed to determine whether more than one genetically distinct population contributes to juvenile aggregations.We examined the temporal genetic heterogeneity of juvenile aggregations collected at four di¡erent times of the year at a nursery area in coastal waters of the Andaman Sea in Trang province, Thailand. Also, we examined the di¡erences between these temporal samples and an outgroup collected from the Gulf of Thailand (Chantaburi). The genetic variation at six polymorphic microsatellite loci within each sample was moderate, with observed heterozygosities across all loci ranging from 0.551 to 0.629 and number of alleles per locus ranging from 7.0 to 8.33. Results indicated substantial genetic di¡erences between the two geographically distant samples, Trang and Chantaburi (Fst 5 0.040^0.050, Po0.005), and between the July sample and the remaining samples from Trang (Fst 5 0.096^0.106, Po0.005). The observed temporal genetic heterogeneity of E. coioides juveniles may re£ect high variability in the reproductive success of each spawning event and the existence of spatially isolated groups of spawners.
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Keywords: Epinephelus coioides, orange-spotted grouper, genetic heterogeneity, microsatellite markers.
Introduction Orange-spotted grouper, Epinephelus coioides (Pisces: Serranidae), is an economically and ecologically signi¢cant species. This species is widely distributed throughout the Indo-West Paci¢c from the Red Sea to South Africa, east to Palau and Fiji, north to the Ryukyu Island and south to the Arafura Sea and Australia (Heemstra & Randall 1993). It is one of the major species targeted for capture worldwide (e.g. Grandcourt, Al Abdessalaam, Francis & Al Shamsi 2005) and for aquaculture in Asia (e.g. Sadovy 2000). In southeast (SE) Asia, juveniles of serranid species are a valuable commodity because of the high demand for aquaculture seeds (Sadovy 2000; Mous, Sadovy, Halim & Pet 2006). The numbers of juvenile produced by hatcheries are still very small because of the di⁄culties in nursing the fry to the juvenile stage (e.g. Duray, Estudillo & Alpasan 1997). Therefore, populations of E. coioides worldwide are under immense ¢shing pressure for both adults (e.g. Grandcourt et al. 2005) and juveniles (e.g. Sadovy 2000; Mous et al. 2006). Because of this heavy ¢shing pressure and life-history characteristics that make them vulnerable to overexploitation (e.g. long life span, slow growth, spawning in aggregation, highly-skewed
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Temporal genetic heterogeneity of juvenile orange-spotted grouper P Pumitinsee et al.
sex ratio favouring females), this species has been listed as near threatened under the Red List of Threatened Species (e.g. IUCN, http://www.iucnre dlist.org/). Thailand is a major supplier of grouper juveniles within SE Asia, and supports demand from other Asian countries (Sadovy 2000). Our study site in Trang province is one of the three large nursery areas in the Andaman coast of Thailand (Janekarn & Kirboe 1991). It supports year-round natural occurrence of juveniles and is a major harvest site (at least 400 000^500 000 individuals purchased from this site in 1999, Sadovy 2000; Sheri¡ 2004). Sustaining the harvest of this life stage may contribute to population declines. This aspect of management, however, is often overlooked (Mous et al. 2006). An understanding of the biology and genetic structure of recruits and juveniles will provide additional insights into the dynamics of adult populations. Our study will provide genetics data relevant to the genetic heterogeneity of juvenile E. coioides collected within a major harvest site within SE Asia (Sadovy 2000) and may provide preliminary evidence for the potential genetic contribution of more than one spawner stock. There is still a large gap in the knowledge of the reproductive biology of tropical serranid species and the linkages between the dynamics of spawning adults in the oceanic environments and the abundance of juveniles present at a nursery area. The existing literature for serranid ¢sh suggests that the spawning habitats for E. coioides adults and nursery sites for juveniles can be far apart (Gillanders, Able, Brown, Eggleston & Sheridan 2003), with adults inhabiting oceanic environments and juveniles inhabiting the mangrove and rocky reef estuarine environments (Sheaves 1995). Most of the knowledge of the reproductive and population ecology of serranid ¢sh was derived from temperate species (e.g. dusky grouper, Epinephelus mariginatus, Marino, Azzurro, Massari, Finola & Mandich 2001; Nassua grouper, Epinephelus striatus, Sadovy & Colin 1995), and our current understanding of tropical grouper species is quite limited. For example, ¢sh stock assessment data have only been reported for E. coioides populations within the Arabian Gulf (e.g. Grandcourt et al. 2005). Despite their economic signi¢cance, there are only a few genetic studies of serranid species (e.g. Stevenson, Chapman, Sedberry & Creswell 1998; Rhodes, Lewis, Chapman & Sadovy 2003; Zatco¡, Ball & Sedberry 2004) and even fewer focused on tropical
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species (e.g. Koedprang & Ngamsiri 2002; Antoro, Na-Nakorn & Koedprang 2006; van Herwerden, Choat, Dudgeon, Carlos, Newman, Frisch & van Oppen 2006; Koedprang, Na-Nakorn, Nakajima & Taniguchi 2007). Very few were designed to examine the genetic variation in recruits and juveniles. Genetic data generated from previous studies indicated a strong spatial di¡erentiation among populations (e.g. Rhodes et al. 2003; Zatco¡ et al. 2004; Antoro et al. 2006). Within the SE Asia region, Antoro et al. (2006) discovered a strong spatial population genetic structure in E. coioides, at a regional scale (Thai vs. Indonesian coastal waters), although allozyme analysis could not distinguish populations located in close proximity (within Trang province, Thailand, Koedprang & Ngamsiri 2002). The dispersal and movements of larvae and juveniles may be important factors determining the levels of genetic isolation among adult populations. A conventional assumption is that populations of a marine species are well mixed through the dispersal of pelagic larvae and juveniles because of the lack of observable geographical barriers. However, there is an increasing evidence for genetic heterogeneity and family structure within and between recruits of several marine species (Li & Hedgecock 1998; Planes & Lenfant 2002; Pujolar, Maes & Volckaert 2006). Several authors suggested that high variation in reproductive success among families is a major contributing factor to such observations (e.g. Hedgecock 1995; Planes & Lenfant 2002; Pujolar et al. 2006). The availability of high-resolution genetic markers, such as microsatellite DNA markers, and powerful statistical models (such as kinship assignment methods; reviewed in Manel, Gaggiotti & Waples 2005) allows for the detection of genetic variation at a ¢ne geographic scale in both freshwater and marine species (e.g. Gadus morhua, Knutsen, Jorde, Andre & Stenseth 2003; Pangasianodon hypophthalmus, So, Maes & Volckaert 2006).We therefore utilized microsatellite genetic markers and a variety of statistical models to evaluate potential genetic isolation among juvenile aggregations present at di¡erent times within a year. The objectives of this study were to describe genetic variation within and among groups of juveniles collected at di¡erent times in a year and to determine potential genetic isolation among spawner stocks, from which the juveniles were produced. Genetic data will provide insights on the dynamics of habitat use of a nursery area by genetically distinct groups of juveniles. Our ¢ndings can help de¢ne
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Aquaculture Research, 2009, 40, 1111^1122 Temporal genetic heterogeneity of juvenile orange-spotted grouper P Pumitinsee et al.
criteria for the design of protected areas and for ¢sheries management of this species for aquaculture.
Material and methods Study area and sampling scheme We collected orange-spotted grouper (E. coioides) juveniles (total length 5 6.6^28.0 cm) from Trang (UTM 47N0555556/0805863; N 5 252) and Chantaburi (UTM47N1385415/0821685; N 5 43) provinces, Thailand (Fig. 1). Both locations are important har-
vest sites for juvenile grouper, with the Trang site being one of the largest nursery grounds for grouper juveniles in Thailand. We analysed ¢ve samples, including four temporal samples from Trang and an outgroup from Chantaburi. We collected the Trang samples in January (JA, total length 515.0^25.5 cm, mean total length 5 20.29 2.65 cm), April (AP, total length 513.5^22.5 cm, mean total length 517.94 2.44 cm), July (JU, total length 514.0^27.0 cm, mean length 5 20.42 3.45 cm) and November (NO, total length 515.4^28.0 cm, mean total length 5 19.78 2.38 cm) 2004 and the Chantaburi sample
Figure 1 Map of Thailand showing sampling locations, coastal waters of Trang and Chantaburi provinces.
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Aquaculture Research, 2009, 40, 1111^1122
Table 1 Microsatellite genetic variation at six loci of Epinephelus coioides samples collected in January (JA), April (AP), July (JU) and November (NO) 2004 from Trang and Chantaburi (CH) provinces Month collected (Trang samples) Loci CA02
CA06
CA07
EM07
EM08
EM10
All loci
N A AE H0 He Fis N A AE H0 He Fis N A AE H0 He Fis N A AE H0 He Fis N A AE H0 He Fis N A AE H0 He Fis A AE H0 He Fis
January (JA)
April (AP)
July (JU)
November (NO)
CH
60 7 2.25 0.617 0.555 0.112 60 11 4.888 0.683 0.792 0.141 60 4 1.166 0.150 0.143 0.056 59 7 4.44 0.775 0.781 0.016 59 4 2.169 0.610 0.539 0.132 55 17 10.522 0.673 0.905 0.257 7.83 4.238 0.583 0.618 0.067
60 5 1.91 0.483 0.476 0.016 59 12 4.685 0.787 0.793 0.117 59 4 1.249 0.153 0.199 0.235 56 6 4.698 0.804 0.787 0.021 58 3 1.913 0.500 0.477 0.048 55 21 12.449 0.673 0.920 0.269 8.17 4.484 0.551 0.608 0.102
72 7 3.22 0.712 0.690 0.033 72 9 4.803 0.945 0.792 0.194 67 4 1.095 0.075 0.087 0.140 70 6 4.030 0.775 0.752 0.030 61 3 1.805 0.311 0.446 0.302 66 20 11.153 0.627 0.910 0.311 8.167 4.352 0.574 0.613 0.063
56 6 2.408 0.589 0.590 0.008 58 9 4.030 0.603 0.780 0.226 59 4 1.071 0.051 0.066 0.232 59 7 4.509 0.780 0.778 0.002 55 5 2.167 0.509 0.539 0.055 59 20 9.76 0.712 0.898 0.207 8.33 4.076 0.541 0.607 0.119
43 6 4.12 0.644 0.757 0.149 40 9 5.040 0.829 0.802 0.035 41 3 1.180 0.163 0.153 0.065 41 6 4.344 0.878 0.770 0.141 41 3 1.757 0.463 0.431 0.075 37 15 9.596 0.795 0.896 0.113 7 4.339 0.629 0.635 0.021
Average across all samples
6.2 2.78 0.619 0.612 0.005 352 10 4.97 0.751 0.791 0.051 3.8 1.149 0.118 0.127 0.064 339 6.4 4.404 0.80 0.772 0.036 3.6 1.962 0.479 0.486 0.02 18.60 10.78 0.699 0.906 0.228
Within-population genetic variation indices include the number of alleles per locus (A), e¡ective number of alleles per locus (AE), observed heterozygosity (H0), expected heterozygosity (He), and Fis (positive values indicate heterozygote de¢ciency). Values underlined indicate the deviation of heterozygotes from Hardy^Weinberg proportions (at 0.00167oPo0.05 and Po0.00167 with Bonferroni correction for multiple comparisons, 0.05/30, highlighted in bold type). EM10 was removed from further analyses that required Hardy^ Weinberg equilibrium assumptions.
(CH, total length 5 6.6^12.8 cm) in January 2004. Each sample consisted of 43^72 individuals (Table 1). All individuals were caught in commercial traps used by artisanal ¢shery. We preserved ¢n clip tissue samples in 95% ethanol for further genetic analyses.
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Resolving microsatellite polymorphisms We analysed six microsatellite genetic markers (CA02, CA06 and CA07, Rivera, Graham & Roderick 2003; EM07, EM08 and EM10, Nugroho,Takagi, Suga-
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Aquaculture Research, 2009, 40, 1111^1122 Temporal genetic heterogeneity of juvenile orange-spotted grouper P Pumitinsee et al.
ma & Taniguchi 1998). The markers CA02, CA06 and CA07 were originally developed for Epinephelus quernus and consisted of all perfect CA repeats. EM07, EM08 and EM10 were developed for Epinephelus merra and consisted of imperfect GT repeats. The DNA analyses consisted of three steps: (1) DNA extraction, (2) DNA ampli¢cation using the polymerase chain reaction (PCR) and (3) gel electrophoresis.We extracted DNA from ethanol-preserved ¢n clips using a saltingout protocol (adapted from Aljanabi & Martinez 1997). Brie£y, we incubated small pieces of ¢n clip tissue in lysis bu¡er (10 mM Tris HCl, 2 mM EDTA, 0.4 M NaCl, 10% SDS and 20 ng of proteinase K) at 55 1C overnight. We precipitated the lysis protein using 300 mL of 7.5 M ammonium acetate. The DNA was precipitated and washed using 100% and 70% ethanol respectively. DNA was then ampli¢ed using PCR. Polymerase chain reaction was performed in a thermocycler (Hybaid Touchdown, Thermo Hybaid, Franklin, MA, USA) or (Biometra TGradient, Goettingen, Germany). Each reaction mixture (10 mL) contained 1 mL template DNA solution (approximately 50 ng template DNA), 2.5^5 mM of each primer, 1.0 mM MgCl2, 0.01mM of each dNTP, 1 X reaction bu¡er and 0.2^ 0.3 U of Taq polymerase (Invitrogen, Carlsbad, CA, USA). Temperature pro¢les for the PCR consisted of two steps: a seven-cycle pro¢le consisting of denaturing at 94 1C for 1min, annealing at primer-speci¢c temperatures (50^54 1C) for 30 s and elongating at 72 1C for 30 s, and a 33-cycle pro¢le consisting of denaturing at 90 1C for 30 s, annealing at primer speci¢c temperatures for 30 s and elongating at 72 1C for 30 s (adapted from Nugroho et al.1998).We performed electrophoresis of PCR products on a 6% polyacrylamide gel at 900 V for 3 h.We then visualized PCR products on the gel using a silver staining technique (Promega, Madison, WI, USA). To score alleles, we compared the size of DNA fragments with a DNA sequence of the PGEM plasmid (Promega).
Genetic data analysis We analysed the genetic variation within samples using the following indices: allele frequency, number of alleles per locus (A), the e¡ective number of alleles per locus (AE) and heterozygosity. The e¡ective number of alleles were calculated using the formula AE ¼ P1 p2 , where pi is the frequency of an allele at i
each locus (Nei 1987). We tested for the deviation of observed genotype proportions from those expected
under Hardy^Weinberg equilibrium (HWE) using the Markov chain exact tests (Raymond & Rousset 1995) implemented within the software GENEPOP (Raymond & Rousset 1995; P-value estimated from 10 000 dememorization numbers, in 100 batches with 1000 iterations per batch). We also used GENEPOP to test for genotypic linkage disequilibria between pairs of loci within each sample and for the overall sample. To investigate the likelihood of the presence of null alleles, we used the Brook¢eld estimator 1 in the MICROCHECKER software (van Oosterhout, Hutchinson, Wills & Shipley 2004). To assess genetic variation among samples, we performed exact tests of allele frequency di¡erences (Guo & Thompson 1992) and the analysis of molecular variance (AMOVA, Exco⁄er 2001), a test analogous to univariate analysis of variance (ANOVA). Under the AMOVA framework, we estimated, pair-wise, the subdivision index, Fst. We also estimated genetic distance values (Cavalli-Sforza and Edwards’ (1967) genetic distance, and Neighbour-joining cluster analysis with 1000 bootstrap replications).We performed exact tests (1000 permutations) and AMOVA (999 permutations) using GENEPOP software (Raymond & Rousset 1995) and GENALEX 6 (Peakall & Smouse 2006) respectively. We calculated Cavalli-Sforza and Edwards’genetic distance values (1000 bootstrap replications) using Microsatellite Analyzer software (MSA; Dieringer & Schl˛tterer 2003). We constructed a genetic distance tree using the Neighbour-joining clustering method, included in NEIGHBOR and CONSENSE within the PHYLIP software package (Felsenstein 1993). The consensus tree was viewed with TREEVIEW software (Page 1996). To determine the genetic heterogeneity of juvenile samples, we assessed the genetic heterogeneity for both pooled data for all Trang samples and for each prede¢ned temporal sample. To test whether the high variance in reproductive success among families may have in£uenced genetic di¡erentiation among temporal samples, we evaluated half-sibship and fullsibship relationships (i.e. relatedness coe⁄cient (rXY) 0.25 and 0.5 for half-sibs and full-sibs respectively) for every pair of individuals within a sample, using KINGROUP software (version 2; Konovalov, Manning & Henshaw 2004). The KINGROUP software calculates the rXY value for a pair of individuals within a sample and evaluates the likelihood of that value both under a speci¢ed hypothesized relationship (H1) and under the null hypothesis of no relationship (H0). The program uses a simulation approach to determine how large the ratio of the likelihood (H1/H0) must be to reject H0 for a given signi¢cance level (a).
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Temporal genetic heterogeneity of juvenile orange-spotted grouper P Pumitinsee et al.
Results Microsatellite DNA polymorphisms and within-sample genetic variation All the microsatellite markers examined were polymorphic in at least one sample. For each locus, the average number of alleles across all samples ranged from 3.6 (EM08) to 18.60 (EM10), the average e¡ective number of alleles ranged from 1.149 (CA07) to 10.708 (EM10) and the observed heterozygosities averaged across samples ranged from 0.127 (CA07) to 0.80 (CA06) (Table 1). Within each sample, the number of alleles per locus averaged across loci ranged from 7 (CH) to 8.67 (AP and JU), the average e¡ective number of alleles (AE) ranged from 4.076 (CH) to 4.484 (AP), and the average observed heterozygosities ranged from 0.541 (NO) to 0.629 (CH) (Table 1).We found private alleles in three samples, but only one at a frequency 42% (CA07, frequency 5 0.047 in the CH sample). The genotypic frequencies of most samples were consistent with those expected under the HWE, with the exception of one locus (exact tests, Po0.00167 after the Bonferroni correction for multiple comparisons of 30 tests, Rice 1989). Of 30 tests, ¢ve tests deviated from the Hardy^Weinberg proportions (CA06 in JU, and EM10 in all samples, except CH). Most deviations occurred at EM10 (four out of ¢ve tests at this locus). Microchecker analysis (van Oosterhout et al. 2004) for the presence of a null allele suggested that the frequency of a null allele (r-value) at EM10 ranged from 0.053 (CH) to 0.148 (JU). The average r-value for the Trang samples was 0.124. Therefore, we removed genotypes at EM10 from further analyses that required HWE. These analyses included exact tests for allele frequency di¡erentiation among samples, AMOVA and genetic distance estimation. After removing EM10, one of 25 tests deviated from the expectations under HWE (Po0.002, Bonferroni’s correction
Aquaculture Research, 2009, 40, 1111^1122
for 25 tests). The one signi¢cant value was at CA06 for the JU sample, caused by a surplus of heterozygotes (negative Fis value). In addition to the tests for prede¢ned temporal samples in Trang, we tested the genetic heterogeneity within the pooled sample using exact tests for heterozygote de¢ciency at six microsatellite loci. The test results indicated that genotypic frequencies at four out of six loci of the pooled sample deviated from Hardy^Weinberg proportions (overall P-value o0.001). These results implied the admixture of individuals with a distinct genetic background. The loci examined appeared to be independent, as tests for genotypic disequilibria yielded no signi¢cant value across all samples.
Genetic variation among samples Exact tests for allele frequency di¡erentiation at ¢ve microsatellite loci and pair-wise Fst values revealed genetic di¡erentiation between CH and Trang samples (Po0.005 with the Bonferroni correction for 10 tests,Table 2).WithinTrang samples, AMOVA revealed a genetic di¡erentiation among temporal samples (global Fst 5 0.031, Po0.001, contributing to 3% of the total genetic variation). The JU sample was genetically distinct from other Trang samples (Fst 5 0.096^ 0.106; Table 2). Other Trang samples were genetically similar. Cavalli-Sforza and Edwards’ (1967) genetic distance values suggested a similar pattern of population di¡erentiation (Fig. 2), with the CH being genetically distinct from the Trang samples and the JU sample being distinct from the remaining Trang samples. The mean pair-wise genetic relatedness coe⁄cient (rXY) within a group ranged from 0.005 (CH) to 0.005 (AP). The numbers of sibs in all samples were lower than those expected to occur by chance (Table 3).
Table 2 Pair-wise Fst (obtained from AMOVA framework; above the diagonal) and exact test P-values (Fisher’s method; below the diagonal) inferred from ¢ve microcatellite loci (excluding EM10)
JA AP JU NO CH
JA
AP
JU
NO
CH
0.006
0.171 Highly significant 0.424 Highly significant
0.096 0.106
0.000 0.017 0.096
0.050 0.046 0.040 0.048
Highly significant 0.023 Highly significant
Highly significant Highly significant
Highly significant
Values underlined indicate statistical signi¢cance (Po0.005, after Bonferroni correction 5 0.05/10). JA, January; AP, April; JU, July; NO, November and CH, Chantaburi.
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Aquaculture Research, 2009, 40, 1111^1122 Temporal genetic heterogeneity of juvenile orange-spotted grouper P Pumitinsee et al.
Discussion Genetic markers’characteristics and genetic variation within samples The levels of microsatellite polymorphisms observed in temporal samples of E. coioides in this study were comparable to those observed in E. coioides collected previously for the spatial genetic analyses in the SE Asia region (mean H0 5 0.36^0.55, mean allele/ locus 5 7.25^8, Antoro et al. 2006; mean H0 5 0.36, mean allele/locus 5 5.6, Koedprang et al. 2007), but were relatively lower than those observed in populations of other serranid species (e.g. mean H0 5 0.6805, mean allele/locus 518.75 for Epinephelus Mario; mean H0 5 0.664^0.689, average allele/ locus 512.75^16 for Mycteroperca phenax, Zatco¡ January (JA) 688 November (NO) 721
April (AP)
July (JU)
1000
Chantaburi (CH) 0.01
Figure 2 Neighbour-joining clustering of samples based on Cavalli-Sforza’s and Edwards genetic distance. A value on each node indicates bootstrap replications (out of1000) for the node.
et al. 2004; mean H0 5 0.735^0.885, average allele/ locus 519^22.66 for Epinephelus polyphekadion, Rhodes et al. 2003; mean H0 5 0.51^0.69, mean allele/locus 5 7.71^13.86 for Epinephelus marginatus, de Innocentiis, Sola, Cataudella & Bentzen 2001; and mean H0 5 0.52 for Epinephelus malabaricus, Koedprang et al. 2007). Low e¡ective population size (Ne) due to several life-history traits, such as a highly skewed sex ratio (Grandcourt et al. 2005) and variance in family size, as well as limited gene £ow among populations (Antoro et al. 2006) may have contributed to the below average genetic diversity of E. coioides. We observed heterozygous de¢ciency at EM10 in all Trang samples, although Antoro et al. (2006), whose samples also included a Trang sample, did not detect such pattern in their Trang sample (10^15 cm juveniles collected from May to July 2003). The heterozygote de¢ciency at this locus may be due to the presence of null alleles or selection favouring homozygotes at this particular locus. We ruled out other biological explanations because this pattern of de¢ciency did not occur at other genetic markers. We observed heterozygote excess at CA06 in the JU sample. This observation may be due to the admixture of individuals with di¡erent genetic backgrounds or due to selection at this particular locus in this population. We did not observe a similar pattern at other loci examined for this sample; therefore, the ¢rst hypothesis may be unlikely.
Genetic variation among samples Examined genetic markers were adequately powerful to di¡erentiate Trang and CH samples and to reveal the genetic structure within the Trang samples. The spatial di¡erentiation (Chantaburi vs. Trang) might represent the di¡erentiation between the Andaman
Table 3 Genetic relatedness among individuals within each sample and signi¢cance testing
Sample
No. individuals
Mean rXY
No. pair-wise comparisons
Sig. pairs expected for a0.05
Observed half-sib pairs
Observed full-sib pairs
JA AP JU NO CH
60 60 72 60 43
0.002 0.005 0.001 0.002 0.005
1770 1770 2628 1770 903
88.5 88.5 131 88.5 45
60 61 66 75 32
79 63 103 84 32
Pair-wise comparisons indicate the number of multiple tests per sample per relationship tested. The number of signi¢cant pairs expected for a0.05 is 5% of the number of pair-wise comparisons. Numbers of observed full and half sib-pairs reported indicate the number signi¢cant pairs for a0.05 within a sample. JA, January; AP, April; JU, July; NO, November and CH, Chantaburi.
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Sea and Gulf of Thailand populations; it was consistent with the genetic di¡erentiation patterns observed in E. coicoides (Antoro et al. 2006) and some other marine species in Thailand, such as black tiger prawn (Peneaus monodon; Supungul, Sootanan, Klinbunga, Kamonrat, Jarayabhand & Tassanakajon 2000), tropical abalone (Haliotis asinina; Tang, Tassanakajon, Klinbunga, Jarayabhand & Menasveta 2004) and Asian moon scallop (Amusium pleuronectes, Mahidol, Na-Nakorn, Sukmanomon, Taniguchi & Nguyen 2007). The genetic di¡erentiation could be attributed to the geographic barrier (the Malay Peninsula) between the two coasts of Thailand. An interesting aspect of our results is the temporal genetic variation. Our study is among a few (and the ¢rst for this genus) to illustrate the temporal genetic patchiness of juvenile aggregations in marine ¢sh over a relatively short time scale (within a year). The level of genetic di¡erentiation between the JU and the remaining Trang samples (Fst values 5 0.096^0.106) was comparable to the di¡erentiation between CH and Trang samples (Fst values 5 0.040^0.050). A combination of factors that may have contributed to this genetic heterogeneity include (1) high variability in reproductive success among spawning events and (2) the presence of genetically distinct spawner groups. In several marine ¢sh species, genetic markers (i.e. allozyme and microsatellites) have revealed an unpatterned genetic heterogeneity among cohorts at microgeographic and temporal scales (i.e. genetic patchiness; Planes & Lenfant 2002; Planes, Lecaillon, Lenfant & Meekan 2002; Pujolar et al. 2006; Selkoe, Gatnes, Caselle & Warner 2006). Most studies hypothesized that such genetic patchiness was driven partly by a large variability in the reproductive success among spawners (i.e. the sweepstakes reproductive success hypothesis; Hedgecock 1995). Planes et al. (2002) detected high coe⁄cients of relationship (rXY) in some recruits of a reef ¢sh species (Naso unicornis) present at a site over a short time scale (from 71 to 78 days). Selkoe et al. (2006) detected family structure within cohorts of kelp bass (Paralabrax clathratus) and spatial genetic heterogeneity among sites. In our case, the life-history characteristics of E. coioides that may have allowed a high variation in individual reproductive success include high fecundity (104^106 eggs female 1 in some serranid species; e.g. Whiteman, Jennings & Nemeth 2005), broadcast spawning and the dependence on oceanic conditions for dispersal of larvae. Although the allele frequency di¡erences may be partly attributed to high variability in individual reproductive success,
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we cannot completely rule out other explanations, such as the presence of more than one source population. Juveniles collected in di¡erent sampling months might have derived from distinct groups of spawners. This distinction can be temporal or spatial. In some marine species, genetic evidence supports the hypothesis of ‘spawning waves’, where di¡erent groups of spawners use the same spawning ground at di¡erent times (e.g. herring, Clupea herengus; Jrgensen, Hansen & Loeschcke 2004; European eel, Anguilla anguilla L.; Maes, Pujola, Hellemans & Volckaert 2006). In herring (Clupea herengus L.) in the Baltic Sea, Jrgensen et al. (2004) found that spawners present earlier (March, April) vs. later (May) in the spawning season were genetically distinct (0.0019 oFsto0.0136) at one of the two study sites. If this phenomenon exists for E. coioides in the Andaman Sea, our results may suggest that the juveniles we sampled resulted from at least two spawning episodes: (1) the JA, AP and NO samples (spawned during November to May, assuming a growth rate of fry of 2^3 cm month 1, Rimmer 2000) and (2) the JU sample (spawned during August^September of the previous year). Spawners that produced JA and AP juveniles in 2004 may be genetically similar to the ones that produced NO juveniles of the same year. Based on our understanding of the reproductive biology of other grouper species, aggregations of individuals within a population may spawn asynchronously (e.g. Levin & Grimes 2002). Peak spawning activities of each aggregation are often associated with lunar cycles (e.g. Levin & Grimes 2002). Based on growth data inferred from otoliths and on behaviour data, the actual spawning time for each individual can be very brief, ranging from days to weeks (reviewed in Levin & Grimes 2002), even though indices of gonad maturation (e.g. gonado-somatic index) indicated that mature gonads may be retained for a period of 1^5 months in a year. The patterns of spawning activities of E. coioides in the Andaman Sea are currently unknown. In some areas, this species forms spawning aggregations. In contrast, Sudaryanto and Mous (2004) observed that E. coioides spawned in pairs in cages located in a bay (Komodo, Flores, Indonesia), with males initiating the spawning activities. The authors also observed spawning activities from September through March in some cages and April through July in others (2^5 times year 1 cage 1). It was not clear how many times one individual could spawn within a year and whether spawning activities were induced by biological (e.g. age,
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Aquaculture Research, 2009, 40, 1111^1122 Temporal genetic heterogeneity of juvenile orange-spotted grouper P Pumitinsee et al.
social hierarchy, genetics) or environmental factors (e.g. temperature or salinity). Our results do not exclude the possibility that the two groups of juveniles (JU vs. other samples) might have originated from two spawning grounds. Oceanic currents may have brought the planktonic larvae from more than one source population to this nursery area at di¡erent times of the year. Sea currents appear to in£uence the population structure of several marine species. For example, in the Gulf of Thailand (east coast of the Malay Peninsula), the northeast monsoon (November^April) forcing the sea current from the south China Sea to the Gulf of Thailand might have facilitated the gene £ow between two distant populations in Indonesia (Antoro et al. 2006). In the Andaman Sea (west coast of the Malay Peninsula), two water masses are present year round: the southern water mass from the Malacca Strait and the northern water mass from the Indian Ocean (Khokiattiwong, Limsaichol, Petpiroon, Sojisuporn & Kjerfve 1991; Limpsaichol & Khokiattiwong1991). The locations where the two masses meet vary by the in£uence of the two monsoons: (1) the northeast monsoon (November^April) and (2) the southwest monsoon (May^October). The northeast monsoon generally forces the sea surface current in the Andaman Sea to move o¡shore, which drives the southern water northward to the southwest corner of Phuket Island (north of this nursery site). The southwest monsoon, on the other hand, drives the northern water inshore and reduces the contribution of water coming from the Malacca Strait. Based on these patterns of water movement, it is possible that our JA, AP and NO samples (approximately 7^10 months old; larva stage in November^May) originated from populations in the Malacca Strait while the JU sample came from a population in the Andaman Sea. To prove this hypothesis, we will need to test genetic compositions of adults collected in prospective spawning areas. Implications for aquaculture, ¢sheries management and conservation Our study is among a few to address the temporal genetic variation of recruits in tropical serranid species. It is clear from our study and that of Antoro et al. (2006) that population genetic structure exists among populations inhabiting the Andaman Sea and the Gulf of Thailand. Our study also provided some evidence for temporal genetic heterogeneity, possibly re£ecting high variability in individual reproductive success, and the presence of genetically
distinct spawning groups. This paper highlights the importance of nursing grounds in accommodating juveniles from genetically distinct stocks. Juveniles of similar sizes present throughout the year at the Trang nursery ground may derive from at least two genetically distinct spawner groups. Determining the population sources of these juveniles will require an additional exploration. For an aquaculture industry that relies heavily on wild-caught seeds, it is important to recognize the potential impacts of the aquaculture on the sustainability of the wild stocks. To be able to sustain both wild populations and the aquaculture, we will need to appropriately manage the wild harvests as well as seek ways to release the pressure on wild populations. Our study suggested that timing for the seed collection may be an important consideration (dry vs. rainy seasons). Given that E. coioides is on the Red List of Threatened Species (IUCN), the wild seeds alone probably will not meet the increasing demands of the aquaculture of this species. The production of ¢ngerlings from hatcheries could ensure the long-term supplies of the aquaculture seeds. Even though the current hatchery technologies still have not allowed mass production of fry and ¢ngerling, research in relevant areas, such as larviculture techniques, broodstock husbandry, reproduction, nutrition and genetics, is extremely crucial. For example, the development of domesticated broodstocks, raised from hatchery-produced fry to adults, may enhance some characteristics that allow individuals to survive well in aquaculture environments. We observed such improvements in some other marine ¢sh species, such as Japanese £ounder (Paralichthys olivaceus; e.g. Shimada, Murakami, Tsuzaki & Seikai 2007), tiger grouper (Epinephelus fuscoguttatus; e.g. Liao 2000) and snapper species (Lujanus spp.; e.g. Liao 2000). Our results raise several biological and management questions for this species: (1) Is this temporal genetic structure stable? (2) Do di¡erent spawning groups share the spawning grounds? (3) If di¡erent genetic stocks exist, how di¡erent are their demographic characteristics? (4) How di¡erent is the ¢shing pressure on genetically distinct stocks? Both genetic and demographic information will be necessary for sustainable ¢sheries management of this species. Acknowledgments We would like to thank the Thailand Research Fund for funding this research through the project entitled
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‘Application of Genetics and Biotechnology for Sustainable Development of Aquaculture’ (Senior Research Scholar 2004) awarded to U. Na-Nakorn. Graduate School and Faculty of Science of Burapha University provided partial funding for P. Pumitinsee. We greatly appreciate the insightful discussion and comments on the manuscript from Dr J. Ovenden at the Department of Primary Industry and Fisheries, Australia, and Dr L. Miller at the University of Minnesota, United States, and D. J. Anderson at Burapha University. We thank anonymous reviewers for improving the quality of the manuscript.
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