© 2006 International Council for the Exploration of the Sea
Population genetic structure of crimson snapper Lutjanus erythropterus in East Asia, revealed by analysis of the mitochondrial control region
a AMBL&LED, South China Sea Institute of Oceanography, Chinese Academy of Sciences 164 Xingang Road, Guangzhou 510301, P.R. China
b Institute of Oceanography, Chinese Academy of Sciences Qingdao 266071, P.R. China
*Correspondence to J. Zhang: tel: +86 20 89023219; fax: +86 20 84451672. e-mail: jbzhang30{at}vip.sina.com.
The population genetic structure of the crimson snapper Lutjanus erythropterus in East Asia was examined with a 427-bp hypervariable portion of the mtDNA control region. A total of 262 samples were collected and 75 haplotypes were obtained. Neutrality tests (Tajima's and Fu's) suggested that Lutjanus erythropterus in East Asia had experienced a bottleneck followed by population expansion since the late Pleistocene. Despite the low phylogeographic structures in mtDNA haplotypes, a hierarchical examination of populations in 11 localities from four geographical regions using analysis of molecular variance (AMOVA) indicated significant genetic differentiation among regions (
CT = 0.08564, p < 0.01). Limited gene flow between the eastern region (including a locality in the western Pacific Ocean and two localities in the East Sea) and three geographic regions of the South China Sea largely contributed to the genetic subdivision. However, comparisons among three geographic regions of the South China Sea showed little to no genetic difference. Populations of Lutjanus erythropterus in East Asia are inferred to be divided into two major groups: an eastern group, including populations of the western Pacific Ocean and the East Sea, and a South China Sea group, consisting of populations from northern Malaysia to South China. The results suggest that fishery management should reflect the genetic differentiation and diversity in East Asia.
Keywords: East Asia, Lutjanus erythropterus, mtDNA control region, population genetic structure
Received 2 May 2005; accepted 18 January 2006.
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Introduction |
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The genetic structure of fish populations has attracted considerable interest, not only because of a fundamental interest in biotic evolution (Tudela et al., 1999), but also because of its importance for the management of fisheries (Bailey, 1997; Roldan et al., 2000). Determination of population genetic structure is essential information to underpin resource recovery and to aid delineating and monitoring populations for fishery management (Roldan et al., 2000).
The commercially important crimson snapper (also called scarlet snapper), Lutjanus erythropterus, is widely distributed throughout the Indian Ocean and the tropical parts of the western Pacific Ocean, ranging from India through the entire Malay Archipelago to China, the Philippines, and Australia (Masuda, 1984; Newman et al., 2000). It is often captured in the East Sea and South Japan, most likely carried by the warm Black Current from the tropical areas of the Pacific Ocean (Chen and Wei, 1991). However, the genetic pattern among populations is still poorly known for most marine species within East Asia (Lourie and Vincent, 2004). There is limited information about the breeding activity, which can give a clue to its population genetic structure (Zhang and Lu, 1985). The species, which lives mainly near coral reefs, has been observed moving away from the reefs towards the shallow areas during warmer seasons to breed, moving back to the deeper areas in the cooler seasons (Zhang and Lu, 1985). Like most fish of the genus Lutjanus, the crimson snapper has a pelagic larval stage of about three weeks before settling to the bottom for shelter (Oshima, 1987). Many explanations for genetic structuring between populations of marine species on local and regional scales involve the influence of pelagic early life stages (Chenoweth and Hughes, 1997; Dudgeon et al., 2000; Bernardi et al., 2001), and the pelagic larval dispersal ability is theoretically associated with the level of gene flow (Leis, 1991). Indeed, marine species that lack phylogeographic structure generally have high larval dispersal potential (Palumbi, 1994; Hellberg et al., 2002). However, there are a number of exceptions because of biological mechanisms, water dynamics, or historical events (Shulman and Bermingham, 1995; Shulman, 1998; Barber et al., 2000; Nelson et al., 2000).
While direct methods such as fishery investigations and tagging studies have the advantage of providing a contemporary estimate of the population on an ecological time scale, population genetic analyses can provide insights into historical population demography (Slatkin, 1994). Population genetic structure can be used to examine the spatial components of phylogeographical lineages and to explain the evolutionary process of geographically related populations (Avise, 2000; Emerson, 2002). Phylogeographic analyses based on DNA sequencing provide insights into historical aspects of geographic structure by overlaying phylogenetic patterns on spatial distributions of populations (Avise, 2000). Such studies could greatly facilitate application of comprehensive theories such as evolution to the design of conservation strategies (Avise, 1989; Shanker et al., 2004). Mitochondrial DNA (mtDNA), owing to its fast mutation rate, has proved to be extremely useful in population studies, high nucleotide and haplotype diversity providing the necessary variation to explore parameters such as subdivision and gene flow (Avise et al., 1987; Wang et al., 2000; Schultheis et al., 2002). In the present study, the sequence analysis of the mtDNA control region was employed to explore phylogenetic relationships among populations from 11 localities and to reveal the population genetic structure of Lutjanus erythropterus in East Asia.
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Material and methods |
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TopIntroductionMaterial and methodsResultsDiscussionReferences |
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Sampling
A total of 218 adults was collected in 11 localities from four geographical regions (Figure 1): (i) the eastern region, including a locality in the western Pacific Ocean, east to the Philippines (PHE), and localities in the East Sea (Zhoushan, ZS, and Xiamen, XM), and in the South China Sea: (ii) the northern region, including localities near Yiangjiang (YJ) and North Bay (NB); (iii) the central region, including localities near Hainan Island (HN), Huangyan Coral Reef (HY), and Xisha Island (XS); and (iv) the southern region, including localities near Bac Lieu (Vietnam, BL), Half Moon Coral Reef (HM), and Sterra Coral Reef (SR). Adults from PHE, ZS, XM, YJ, NB, HN, and BL were obtained with the help of commercial fishers operating in May 2002, and from HY, XS, HM, and SR during the CINS project (Comprehensive Investigation of Nansha Coral Reefs) in May and June 2002. In addition, 44 juveniles were collected at HN and BL in August 2003. All samples were preserved in 70% ethanol.
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DNA extraction and PCR
The dorsal muscle (15–20 mg) of each sample was dissected and rinsed twice with 1 ml TE buffer (10 mM Tris, 1 mM EDTA/pH 8.0). Total genomic DNA was extracted using a DNA Extraction Kit (QIAGEN) and stored in AE buffer (50 mM sodium acetate, 10 mM EDTA/pH 5.3) at –30°C.
Primers A (5'-ATTCCA CCTCT AACTC CCAAA GCTAG-3') and G (5'-CGTCG GATCC CATCT TCAGT GTTAT GCTT-3'; Lee et al., 1995) were adopted to amplify the whole control location flanked by partial tRNA pro gene and tRNA phe gene of the mitochondrial DNA. Each PCR reaction was performed in a volume of 25 µl containing 10–30 ng template DNA, 2.5 µl of 10x reaction buffer, 2.5 µl of MgCl2 (25 mM), 1.0 µl of dNTPs (10 mM), 10 pmol of each primer, and two units of Taq DNA polymerase (Promega) in a GeneAmp 2400 thermal cycler (Perkin–Elmer). Initial denaturation was for 5 min at 94°C, followed by 35 cycles of 45 s at 94°C, 90 s at 55°C, and 1 min at 72°C. A final elongation was carried out for 10 min at 72°C. PCR products were separated on a 1% agarose gel and purified with QIAquick Gel Extraction Kit (QIAGEN). The targeted DNA was cloned into the pMD-18 T vector (Takara). The ligated vector was cloned into DH5
cells, and the cells were cultured on Luria–Bertani (LB)/ampicillin plates with X-gal and IPTG (Promega) overnight. Positive colonies were picked up and sequenced with ABI 3700 DNA sequencer using the Big Dye Terminator Cycle Sequencing Reaction Kit and M13 primers.
Sequences of the entire control region were aligned with CLUSTAL W 1.8 (Thompson et al., 1994) using the default settings, and a hypervariable portion was identified in the control region. Primers MCI-L1 5'-TTCATTAACATGTTCTAGGGAC-3' and MCI-L2 5'-GGTGGGTAACGAGGAGTATG-3' were designed in the conserved flanking regions, and used in the nested PCR for amplifying the hypervariable portion in other samples. The procedures of the nested PCR were: initial denaturation of 5 min at 94°C, 30 cycles of 30 s at 94°C, 40 s at 55°C, and 50 s at 72°C, and a final elongation of 10 min at 72°C. A volume of 1 µl amplification product with primers A and G was used as the template in the nested PCR.
Data analyses
ARLEQUIN 2.0 (Schneider et al., 2000) was used to perform the analysis of molecular variance (AMOVA; Excoffier et al., 1992) and to estimate statistics, which incorporate information on nucleotide differences between haplotypes. The significance of statistics for population comparisons was assessed using 1000 permutations. The nucleotide diversity (
) and the haplotype diversity (h) within populations were also calculated. Estimate of the time since population expansion (t) was calculated as t = 
/2u. was calculated following the equation in Roger and Harpending (1992), where u = 2µ0k, where k is the number of nucleotides assayed, and µ0 is the mutation rate per site per nucleotide.
MODELTEST 3.6 (Posada and Crandall, 1998) was used to determine the optimal substitution model for the mtDNA data, and a hierarchical test of likelihood (LRT) under 56 different models of evolution was performed. The parameter values estimated by MODELTEST 3.6 were adopted for further analysis, including the phylogenetic relationship of haplotypes, the analysis of molecular variances (AMOVA), and the estimates of gene flow. A neighbour-joining (NJ) tree was constructed with the MEGA program (Version 2.1; Kumar et al., 2001), with Lutjanus campechanus (Genbank AF356770 [GenBank] ) and Lutjanus griseus (Genbank AY245010 [GenBank] ) as outgroups. Bootstrap values for an NJ tree were estimated with searches using 1000 replicates, the gap with pairwise deletion.
Nested clade phylogeographical analysis (NCPA) can circumvent difficulties common to many applications of intraspecific phylogenetics (Sivasundar et al., 2001). Two programs were used to perform nested clade analysis. First, TCS 1.18 (Clement et al., 2001) was used to estimate a network of relationships among haplotypes. This program was used to obtain a 95% confidence limit for parsimony (Templeton et al., 1995), as well as to construct a cladogram that showed the nested structure of the haplotypes. Program GEODIS 2.2 (Posada et al., 2000) was used to calculate NCPA distance measures and tests for significant association between genealogy and geography. The nesting structure, together with information on geographical distribution of the haplotypes, was used to estimate two distance measures: the clade distance (Dc), and the nested clade distance (Dn). Dc measures the geographical extent of a given clade, and Dn measures how widespread a given clade is relative to the distribution of its sister clades in the same nesting group. The distinction between tip and interior clades allows testing of the hypothesis of random geographical distribution by permutation tests (10 000 permutations in this study). An updated inference key (Templeton, 2004) was used to interpret the geographical association of haplotypes/clade at all clade levels where significant associations were found.
Immigrations of the gene flow between the four regions were analysed with the software MIGRATE 2.0.3 (Beerli, 2004), which is a maximum likelihood estimator based on the coalescent theory. It uses a Markov Chain Monte Carlo approach to investigate possible genealogies with migration events. The following search strategy was adopted: 10 short chains of 20 000 steps each were run, followed by three long chains of 100 000 steps each. To increase the reliability from the searches of the maximum likelihood surface, we used 10 "heating" chains (1–10 with a linear increase). The number of effective immigrants (
) was calculated from = 
M (Beerli and Felsenstein, 2001). The population parameters (the estimate of the effective population size, = 2Nefµ) and M (the rate of migration, M = 2Nefm) were computed, where Nef is the effective population size of females, µ is the mutation rate per site per generation, and Nefm is the number of immigrant females per generation.
To check for the deviations from neutrality, Tajima's D (Tajima, 1989) was calculated to assess evidence for population expansion, as was Fu's test of neutrality (Fs; Fu, 1997). Meanwhile, the concordance of data with the distribution underlying the expansion model of Rogers (1995) was assessed. Population demographic history was examined by calculating mismatch distributions (Li, 1977; Harpending, 1994; Rogers, 1995) overall, using the pairwise difference method in ARLEQUIN 2.0.
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Results |
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TopIntroductionMaterial and methodsResultsDiscussionReferences |
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Genetic diversity
The complete control region sequence, along with portions of the flanking tRNA pro and tRNA phe genes, was obtained from 16 samples of the crimson snapper (GenBank accession number AY664519 [GenBank] –AY664529 [GenBank] andAY664530–AY664534 [GenBank] ). The DNA fragment containing the control region ranged from 842 to 847 bp, with a consensus length of 849 bp. As a rule, the mtDNA control region consists of parts of conserved sequences blocks (CSBs) and variable segments (Lee et al., 1995); this has been demonstrated for many fish (Wang et al., 2000; Aurell and Berrebi, 2001). For Lutjanus erythropterus, a segment of high variability (hypervariable portion) flanking conserved regions was detected near the tRNA pro gene from 5' to 3' of the light strand, with an alignment length of 427 bp. There was a total of 75 haplotypes (GenBank accession number AY796344 [GenBank] –AY796416 [GenBank] and DQ116014 [GenBank] –DQ116015 [GenBank] ) in all 262 samples (Table 1). The haplotype diversity (h) was high for all populations, with values from 0.942 (XS) to 0.975 (BL), but values of nucleotide diversity (
) were generally low [0.022 (ZS) to 0.039 (BL)], particularly for eastern populations ( 
0.026; Table 1).
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Phylogenetic analysis and population genetic structure
Most of the
ST population pairwise comparisons were low and not significant. However, values of ST between eastern populations and others were generally high [all significant ST values (p < 0.01) were included] (Table 2). The population structure was examined by ‘two-categories’ AMOVA tests. First, we divided the groups to natural geographic boundaries: i.e. the eastern region including ZS, XM, and PHE and the northern region including NB and YJ. Second, samples of the eastern region were excluded and the genetic structure of populations in the South China Sea was analysed (Table 3). Significant structuring among the eastern region and the South China Sea was detected (CT = 0.08564, p < 0.01), but not observed among three geographical regions of the South China Sea (CT = 0.05246, p > 0.01). Within-population variation was significant in two hierarchical levels, which is expected given the high numbers of haplotypes found in each population.
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The optimal substitution model F81 + I + G (Felsenstein, 1981) for Lutjanus erythropterus was obtained with MODELTEST 3.6. The parameters for this model were the following: base frequencies: A = 0.359, C = 0.193, G = 0.140, T = 0.308, and
= 0.488. The NJ tree presented a star-shaped topology (Figure 2).
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A network diagram of the 75 haplotypes yielded by TCS 1.18 is shown in Figure 3. The contingency test GEODIS 2.2 (Posada et al., 2000) showed the geographical association for haplotypes at one-step clades 1-5, 1-12, 1-19, and 1-25, and two-step clades 2-1, 2-5, and 2-8. However, higher-level clades showed no significant levels of geographical association except for 3-2 (Table 4). Based on the inference key of Templeton (2004), three inference patterns were obtained in five nested clades representing three hierarchical levels. Clades 1-5, 1-19, 1-25, 2-1, 2-8, and 3-2 indicated geographic association by NCPA analysis, and haplotypes in clade 1-5 were mainly collected in eastern localities (ZS, XM, and PHE). Haplotypes in clade 1-25 were only found in three localities (HN, HM, and SR). Haplotypes in clades 1-19 and 2-8 were found only in the eastern regions (ZS, XM, and PHE). However, clades 1-12 and 2-5 failed to reject the null hypothesis of no association between phylogenetic relationships of haplotypes and their geographical distribution, partly because of the small sample size.
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The relatively low standard deviations (s.d.; Table 5) for all data showed that the maximum likelihood estimates of
and were generally stable over Migrate runs. Estimates of ranged from 0.021 ± 0.005 (eastern region) to 0.103 ± 0.008 (central region). The values of effective immigrants per generation () (from 0.79 ± 0.37 to 106.30 ± 5.28) indicated fluctuation of gene-flow levels among four geographical regions. When values of were overlaid over the geography (see Figure 1), asymmetric migrations between the eastern region and the South China Sea were observed, the immigration from the eastern region to the central was much higher than the opposite direction (15.22 ± 2.66 vs. 0.79 ± 0.37), and the immigration from the eastern to the northern and southern regions also showed the trend.
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Pattern of historical demography
The model of population expansion could not be rejected because of its concordance with the expectation of historically expanding population (1995) when all samples were combined (p[Simulated sum of squared deviations (sim.SSD)
Observed SSD] = 0.611; Figure 4). This outcome was also supported by the low Harpending's Raggedness index (r = 0.006, p = 0.576). The significant negative values of Tajima's D and Fu's F (Tajima's D = –1.18, p = 0.08; Fs = –23.91, p = 0.002) were consistent with population expansion.
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Owing to the paucity of fish fossils, there are few references to correlate mutation in DNA sequence and time among fish. An average mutation rate (µ0) of approximately 3.6% per million years for mitochondrial control regions in fish of the genus Centropomus (Percoidei: Centropomidae) was calculated by Donaldson and Wilson (1998). Some portions of the mitochondrial DNA evolve quickly in mammals, with the mutation rate as high as 10% per million years, particularly on the fast-evolving 5' portion of the control region (Avise, 1994). In addition, an approximate rate of 11–13% per million years for the 5' portion of the control region of white sturgeon was estimated (Brown et al., 1993). ARLEQUIN calculated the value of
as 9.698, and the time since population expansion was estimated to be about 75 000–100 000 years before present (during the late Pleistocene), using the above independent calibrations for the 5' portion of the control region among fish (10–13% per million years). |
Discussion |
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TopIntroductionMaterial and methodsResultsDiscussionReferences |
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Historical demography
On the whole, in contrast to the high haplotype diversity (0.997), the lower nucleotide diversity (0.030) suggests a bottleneck followed by population expansion (Avise, 2000).
The neutrality of the mtDNA control region mutations was rejected on the basis of Tajima's D and Fu's tests. Tajima's D and Fu's tests are sensitive to factors such as bottlenecks or population expansion which would tend to drive the value of Tajima's D and Fs towards more negative values (Tajima, 1996; Nei and Kumar, 2000; Fauvelot et al., 2003; Martel et al., 2004). Indeed, significant negative values of Fs as well as Tajima's D in this study indicated that Lutjanus erythropterus in East Asia had experienced population expansion, since the mismatch distribution based on the mtDNA sequences accorded well with the predicted distribution under a model of expansion (p(SSDobs) = 0.611, r = 0.006). Moreover, the short branch length (SBL = 0.647) in the NJ tree also suggests a recent (young) population genetic divergence for populations of Lutjanus erythropterus in East Asia, as do the fish Thalassma hardwickii and prawn Penaeus monodon in East Asia (Chen et al., 2001; Sugama et al., 2002). Past geological and climatic events have undoubtedly played a major role in terrestrial biogeography, but a similar role for historical geological events has yet to be comprehensively explored for marine species (Lourie and Vincent, 2004). Lowered sea levels (up to at least 120 m below present levels) were associated with Pleistocene glaciations (2.4 Ma–10 000 years ago) which resulted in the formation of land bridges between mainland Asia, the western Indonesian islands, and among the Philippine islands, so greatly limiting gene flow among populations (Voris, 2000). The crimson snapper prefers coral reefs as its habitat (Zhang and Lu, 1985), so the genetic population structure of Lutjanus erythropterus could be linked to the phylogeography of coral reefs in the South China Sea. The islands in the central South China Sea are mainly atoll and barrier reefs developed since the late Pleistocene, whereas the marginal zone from Hainan Island to the coast of South China is mainly coastal and fringing reefs developed since the Holocene (Xia et al., 1985; Huang, 1991). This demonstrates that there is a spatial expansion northwards for coral after the late Pleistocene. Estimation of the time since population expansion of Lutjanus erythropterus in the present study (75 000–100 000 years before present) suggests consistency with a sea level rise since the late Pleistocene (Wen et al., 1999; Huang et al., 2004; Zhong et al., 2004).
Population genetic structure and its implications for fishery conservation
Larval dispersal in marine species has long been assumed to be widespread, resulting in little to no genetic differentiation among widespread populations, because of the lack of obvious geographical barriers to migration in oceanic environments (Avise et al., 1987; Rivera et al., 2004). However, this was not the case in the present study. There was limited gene flow between the eastern region and the South China Sea (Table 5), and significant structuring among the eastern region and the South China Sea (CT = 0.08564, p < 0.01). However, pairwise ST and AMOVA indicated little to no genetic difference among populations of the South China Sea. On the one hand, it was not significant among three geographical regions of the South China Sea (CT = 0.05246, p > 0.01). On the other, it was also not significant among populations within the South China Sea (SC = –0.0447, p > 0.01) when eastern populations were not included. Therefore, Lutjanus erythropterus in East Asia is concluded to be divided into two major groups: an eastern group including populations of the western Pacific Ocean and East Sea, and a South China Sea group consisting of populations from northern Malaysia to the coast of China. Negative values indicate great differences between two random samples from the same population, rather than two random samples from different populations (Arnason and Palsson, 1996).
No strong association with geography and genealogy was dedicated at high nested levels except for clade 3–2. However, three inference patterns were obtained in five nested clades indicating geographic association (Table 4). In the NJ tree, haplotypes 08, 12, 16, 26, 61, 64, and 75, which were restricted to eastern populations, were clustered together (Figure 2). Many marine species show genetic differentiation among South China Sea vs. Pacific Ocean populations, reflecting Pleistocene separation (Lourie and Vincent, 2004). This has been detected in Hardwick's wrasse Thallasoma hardwicki (a fish) as well as the prawn Penaeus japonicus in East Asia (Chen et al., 2001, 2004; Tzeng et al., 2004). The warm oceanic water (Black Current) (shown in Figure 1) from the western Pacific Ocean intrudes northwards from mid-spring to autumn, reaching from the southeastern coastline of China to Japan, and dominating the East Sea and Japan Sea (Fan, 1982; Rehder and Suess, 2001; Tzeng et al., 2004). Therefore, the appearance of tropical species such as Lutjanus erythropterus in the East Sea may be primarily associated with the warm water of the Black Current (Wang and Tzeng, 1997), so it is no surprise that there is genetic homogeneity among populations of the western Pacific Ocean and East Sea.
Fidelity to a spawning site, such as observed in the swordfish Xiphias gladius (Alvarado-Bremer et al., 1996), seems unlikely for Lutjanus erythropterus. Pairwise ST between the juvenile and adult population at the same site were not significantly different from those between the juvenile and adult population from different localities (Table 2). Speciation of population structure in marine fish can be created by the persistence of the early ontogenetic phase in a particular area followed by the homing of adults to specific spawning sites (Sinclair and Iles, 1989). There are many spawning sites along the coastline of China and Vietnam (Zhang and Lu, 1985). Deep population fragmentation would result if larval retention was followed by homing. AMOVA analysis showed no significant genetic differentiation among populations of the South China Sea. It is possible that the AMOVA analysis lacks power owing to the variable, low sample sizes (Alvarado-Bremer et al., 1996). However, most ST values were low and not significant (p < 0.01; Table 2), so it is difficult to refute a conclusion that there are high levels of gene flow for populations of Lutjanus erythropterus in the South China Sea.
Generally, a single panmictic population could recover through increased recruitment by propagation (Munro and Bell, 1997). However, different populations with unique genetic structure should be managed as distinct units, and such units require separate monitoring and management owing to the different levels of gene flow and demographic history (Salgueiro et al., 2003). Gene flow between populations in abundant and widely distributed marine fish species is often relatively high. Therefore, for such species, determining whether the extent of dispersal and gene flow between components of a population complex is low enough to preclude being managed as a single panmictic unit is often difficult (Waples, 1998). It is necessary to minimize the frequency of extirpation of local populations and to sustain population stability, because genetic diversity is a requisite for evolutionary adaptation to a changing environment (Hedrick and Miller, 1992). For the data used for mtDNA analysis in this study, although there was little differentiation between localities, it needs to be recognized that historical population expansion will mask low migration, and therefore bias gene flow estimates upwards. Consequently, it is necessary to employ more genetic data, such as microsatellite DNA analysis, in further studies.
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Acknowledgements |
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This work was supported by Chinese National funding (40306022), Natural Science funding of Guangdong province (04001300), and the Chinese national special fund for Nansha Coral Reefs (no. 2001DIA50041). We thank Prof. Pin Yan, South China Sea Institute of Oceanography, for his help with the paper.
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