Marine Pollution Bulletin 81 (2014) 256–261

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Marine Pollution Bulletin

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Metal pollution status in Zhelin Bay surface sediments inferred froma sequential extraction technique, South China Sea

0025-326X/$ - see front matter � 2014 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.marpolbul.2014.01.030

⇑ Corresponding authors. Address: South China Sea Fisheries Research Institute,Chinese Academy of Fishery Sciences, Guangzhou 510300, China (Q. Lin). Tel.: +8620 89108306; fax: +86 20 84451442. Address: Institute of Hydrobiology, JiNanUniversity, Guangzhou 510632, China (S.-J. Jiang). Tel.: +86 20 89108306; fax: +8620 84451442.

E-mail addresses: hydrobio@163.com (Q. Lin), 114948568@qq.com (S.-J. Jiang).

Yang-Guang Gu a,c,d, Qin Lin a,c,d,⇑, Shi-Jun Jiang b,⇑, Zhao-Hui Wang b

a South China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Guangzhou 510300, Chinab Key Laboratory of Eutrophication and Red Tide Prevention of Guangdong Higher Education Institutes, Jinan University, Guangzhou 510632, Chinac Key Laboratory of Fishery Ecology and Environment, Guangdong Province, Chinad Key Laboratory of South China Sea Fishery Resources Exploitation & Utilization, Ministry of Agriculture, Guangzhou 510300, China

a r t i c l e i n f o a b s t r a c t

Keywords:MetalsChemical speciationSediment analysisEcological riskZhelin Bay

Surface sediments collected from Zhelin Bay, the largest mariculture base of eastern Guangdong Province,were analyzed for total metal concentrations and chemical speciation. The results demonstrated that theaverage total concentration (mg/kg) ranges were 36.7–65.8 (Pb), 53.8–98.8 (Cr), 39.0–87.1 (Ni), 50.9–144.5 (Cu), and 175.0–251.2 (Zn), which were clearly higher with respect to their corresponding bench-mark values. The predominant speciation of Pb was reducible and comprised a residual fraction, whereasa major portion (57.6–95.4%) of Cr, Ni, Cu, and Zn was strongly associated with the residual fractions. Tak-ing as a whole, surface sediments of Zhelin Bay had a 21% probability of toxicity based on the meaneffects range–median quotient.

� 2014 Elsevier Ltd. All rights reserved.

Sediments function as a sink for metals in aquatic ecosystems;they also act as a source of metals when environmental conditionschange (pH, Eh, and others) (Burton, 2002; Chapman et al., 2013;Guo et al., 1997; Woods et al., 2012). As ubiquitous environmentalcontaminants, metals have elicited significant attention becausemany of them are toxic at concentrations over certain thresholds.One of the most serious environmental issues of metals, which dis-tinguishes them from other toxic pollutants, is that they are resis-tant to biodegradation and have potential to bio-accumulate andbecome biomagnified, increasing the exposure of organisms andhumans (Gao and Chen, 2012; Malferrari et al., 2009; Subidaet al., 2013).

The toxicity and mobility of metals in the environment dependstrongly on their specific chemical forms and binding state(Gleyzes et al., 2002). Thus, toxic effects and biogeochemical path-ways can only be studied based on the measurement of theseforms (Quevauviller, 1998). Sequential extraction is an importantand widely used tool that provides information about the strengthof metal binding to particulates and the phase associations of met-als in solid matrix, among others (Gao et al., 2010; Sutherland,2010). Numerous sequential extraction procedures have beendeveloped based on the Tessier protocols. Among these techniques,

the BCR procedure (Community Bureau of Reference, now super-seded by Standards, Measurement, and Testing Program of theEuropean Community) is one of the most widely used, and hasbeen widely adopted and applied in numerous types of solidsamples, including freshwater sediments, salt water sediments,soil, sewage sludge and particulate matter (Gleyzes et al., 2002;Rao et al., 2008; Sutherland, 2010).

Guangdong province is one of the most developed areas inChina. Zhelin Bay, situated in the northeast part of GuangdongProvince, covers an area of 68–70 km2 and a mean water depthof 4.8 m, with a maximum depth of 12 m. This bay is the largestmariculture base of eastern Guangdong and one of the most inten-sively managed estuaries in China, with half of the seawater area(34.6 km2) occupied by oyster or cage fish farms (Wang et al.,2008; Yang and Chen, 2011). The aquaculture industry is the maindriver of local economic growth in this area, and its output valuewas over 2 billion Chinese Yuan in 2011 (RCG, 2012). Althoughaquaculture in Zhelin Bay has significantly contributed to the localeconomy, its development has led to rapid deterioration of theaquatic ecosystem and environment over the past years (Wanget al., 2008; Yang and Chen, 2011). However, few studies on theeffect of aquaculture on metal contamination have been conducted(Qiao et al., 2010; Wang et al., 2013). Besides aquaculture pollu-tion, as population grows and economy develops in recent decades,the Zhelin Bay is also under the stress of environment deteriorationdue to increasing industrial pollution, agriculture activities anddomestic sewage. For example, each year about 627.5 million tonsof sewage discharge that has not undergone strict treatment, 50.3%

Y.-G. Gu et al. / Marine Pollution Bulletin 81 (2014) 256–261 257

from industry, enters Zhelin Bay (CZG, 2007). The sedimentationrate (sediment deposition flux) has significantly increased overthe last few decades (Zhao-Hui. Wang unpublished data). Highsedimentation rate can cause changes in the geochemical proper-ties of surface sediments on an annual scale. To the best of ourknowledge, previous studies have focused on total metal concen-trations in surface sediments of Zhelin Bay, whereas investigationsrelated to metal speciation are scarce. Therefore, the present studyaims to (1) survey total metal concentration and speciation in sur-face sediments in Zhelin Bay, (2) explore the sources and behaviorof metals, and (3) assess their ecological risks.

Sixteen surface sediment samples collected from Zhelin Bay inOctober 2012 roughly following a ‘zigzag’ route during a researchcruise (Fig. 1). All sample sites were located within the active aqua-culture zone, closely adjacent to the earlier aquaculture area thathas become a salt marsh. Surface sediments were obtained usinga 9890 ml volume stainless steel Peterson grab. Surface sedimentof 0–3 cm was carefully collected at each site. Two sets of sampleswere obtained with a plastic spatula and then placed in plastic zip-lock bags. All samples were preserved with ice and immediatelytransferred to the laboratory; one set of samples was kept frozenat �4 �C and another at �20 �C until analysis. The samples frozenat �4 �C were used for grain size analysis. The samples refrigeratedat �20 �C were dried in an oven at 40 �C until constant weight,ground gently with agate pestle and mortar, sieved with 63 lmmesh sieve for homogenization, and then stored in glass bottlesfor organic matter (OM) and metal analyses.

The OM content was estimated by losses on ignition to constantmass (about 6 h) at 550 �C (Frankowski et al., 2002). Pretreatmentsof particle size were carried out based on the China NationalStandards (GB/T12763.8-2007). The sample granulometry wasdetermined using a Malvern Mastersizer 2000 laser diffractometer.The total concentrations of metals were analyzed by X-ray

Fig. 1. Location of sampling sites in

fluorescence spectroscopy (XRF). Pressed powder tablets were pre-pared from ground sediment samples (<63 lm), followed by directelemental determination of the sample (Basta and McGowen,2004). The sequential extraction procedure reported by Rauretet al. (1999) was carried out to obtain information about the frac-tions of metals. This scheme divides the elements into four opera-tionally defined geochemical fractions: acid soluble, reducible,oxidizable, and residual. The detailed procedures for the sequentialextraction used in this study have been described elsewhere (Guet al., 2012b). The concentrations of metals in the four geochemicalfractions were determined by atomic absorption spectrophotome-try (AAS, Hitachi Z2000). The AAS detection limits were calculatedas 3r/S (r is standard deviation of blank signal, and S is the sensi-tivity). The detection limits (mg/kg) were 0.1 (Pb), 0.2 (Cr), 0.1 (Ni),0.05 (Cu) and 0.6 (Ni), respectively.

To verify the accuracy of the sequential extraction procedure,the certified reference material CRM 701 was used and the rangesof the recoveries of Pb, Cr, Ni, Cu, and Zn in the acid soluble, reduc-ible, and oxidizable fractions were 90–107%, 83–91%, and 94–113%,respectively. Another China National Standard material (OffshoreMarine sediment, GBW 07314) was used to verify the XRF recov-ery. Recoveries for the seven metals were between 93% and 109%.

Prior to principal component analysis (PCA), a Q–Q plot of eachvariable was used to evaluate the normality of the dataset. Thedata matrix was then log-transformed to achieve normal distribu-tion or approximate normality. PCA was performed on thestandardized dataset to minimize the effects of differences in mea-surement units or variance and to render the data dimensionless.All analyses were performed on IBM SPSS 19.0 for Windows.

Particle size composition and organic matter content were mea-sured to obtain the general sediment properties in this study. Thesediments were predominantly composed of silt, followed by clay(Fig. 2). The sand content in most of the samples was less than 2%.

Zhelin Bay, South China Sea.

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10

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Fig. 2. Spatial variations of grain size compositions and organic matter in surfacesediments of Zhelin Bay.

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Fig. 3. Spatial variations of selected metals in total concentrations and theirdistributions in different geochemical fractions of surface sediments from ZhelinBay.

258 Y.-G. Gu et al. / Marine Pollution Bulletin 81 (2014) 256–261

The average percentages of silt and clay were 59.2% and 30.3%within the range of 30.3–72.2% and 8.1–37.9%, respectively. The or-ganic matter contents ranged from 6.1% to 16.3% of dry weight,with an average of 12.5%. Relatively higher content of organic mat-ter (S3–S5, S7–S8) was found in the central area of the fish cageculture. Zhelin Bay is an intensive mariculture area for fish andshellfish, resulting in the release of large amounts of dissolvedand particulate nutrients to the surrounding environment (Wanget al., 2008; Wang et al., 2013). Previous studies have demon-strated that the most evident consequences of fish farming on ben-thic environment increases organic matter accumulation in thesediment (Naylor et al., 2000; Pusceddu et al., 2007).

The concentrations of the five metals from all sediment sampleswere 36.7 to 65.8 (Pb), 53.8 to 98.8 (Cr), 39.0 to 87.1 (Ni), 50.9 to144.5 (Cu), and 175.0 to 251.2 (Zn), with average concentrationsof 50.9 ± 9.0, 69.9 ± 13.3, 53.0 ± 13.4, 88.0 ± 30.5, and 204.5 ±19.8 mg/kg, respectively (Fig. 3 and Table 1). The average concen-trations of Pb, Cr, Ni, Cu, and Zn were clearly higher with respectto their corresponding background values (Table 1). Metals mainlyenter the marine environment by rivers through estuaries. The ma-jor contribution of anthropogenic metals in marine coastal areasoriginates from terrestrial sources, i.e., mining, metalworking,industrial processes and urban developments, and other humanactivities near rivers and estuaries (Ip et al., 2007; Sapkota et al.,2008). The higher metal concentrations in Zhelin Bay are due tothe runoffs and sewage discharges by two rivers (Fig. 1), as wellas industrialization and human population. In addition, metalscan be derived from mariculture activities, such as fish feeds, trashfish and dry pellet (Gu et al., 2012b; Sapkota et al., 2008; Zhanget al., 2012). The results from the current study were comparedwith those from other bays/estuaries in China. The average concen-trations of most metals in the sediments of Zhelin Bay are higherthan those in other areas in China, such as Jiaozhou Bay, West Xia-men Bay and Daya Bay (Table 1).

Principal component analysis (PCA) is an effective method to re-duce the high dimensionality of variable space and to better under-stand the relationships among trace elements (Gu et al., 2012a;Reid and Spencer, 2009). In this study, PCA (VARIMAX rotationmode) was used to identify the two principal components (PC)for all the sediment samples, representing 90.97% of the total var-iance of the dataset. The VARIMAX rotation of the matrix can elim-inate ambiguities. In addition, Kaiser–Meyer–Olkin (KMO) andBartlett’s sphericity tests were used to examine the validity ofPCA before interpreting the results. KMO and Bartlett’s results

were 0.75 and 122.74 (df = 21, p < 0.01), indicating that PCA wasuseful in dimensionality reduction.

The loadings of metals on the factors for different datasets arelisted in Table 2. According to the component matrix, Pb, Cr, Fe,and Ti have strong positive loadings on the first component (PC1,46.92% of the total variance). Meanwhile, Ni, Cu, and Zn havestrong positive loadings on the second component (PC2, 44.05%of the total variance). Cu and Zn are essential elements for life(Bruland et al., 1991), and the pollution they cause is most relatedto sewage (Caeiro et al., 2005; Neto et al., 2006). Pb is anthropogen-ically influenced, which is associated with fuel combustion andcoal combustion (Facchinelli et al., 2001; Gao and Chen, 2012;Gu et al., 2012a). Previous study showed that Pb in surface sedi-ments in Zhenlin Bay came primarily from fuel combustion (Wanget al., 2013). Ni, a corrosion resistant metal, is commonly used inalloys and in the manufacture of coins, magnets, and commonhousehold utensils (Gu et al., 2012a). Fe and Ti are major earthelements and rarely enriched by human activities. Consequently,

Table 1Metal concentrations in surface sediments from Zhelin Bay compared with the average metal concentration in sediments of other bays/harbors (mg/kg, dry weight).

Pb Cr Ni Cu Zn

This study Mean, SD 50.9 ± 9.0 69.9 ± 13.3 53.0 ± 13.4 88.0 ± 30.5 204.5 ± 19.8Median 51.8 69.8 49.1 81.0 202.6Range 36.7–65.8 53.8–98.8 39.0–87.1 50.9–144.5 175.0–251.2

Jiaozhou Baya Mean 24.2 79.3 29.2 29.7 71.2West Xianmen Bayb Mean 50 75 37.4 50 150Daya Bayc Mean 45.7 – 31.2 20.8 113Background valuesd Mean 32.2 28.6 14.8 15.8 57.8Class I upper limite 60 80 – 35 150Class II upper limite 130 150 – 100 350Class III upper limite 250 270 – 200 600ERL guidelinef 47 81 20.9 34 150ERM guidelinef 218 370 51.6 270 410

‘‘–’’: Means not available.a Deng et al. (2010).b Zhang et al. (2007).c Yu et al. (2010).d Qiao et al. (2009).e The criterion of China National Standard for marine sediments (GB18668-2002) SEPA (2002).f ERL (Effects Range Low) guideline values indicate concentrations below which adverse effects on biota are rarely observed and ERM (Effects Range Low) guideline values

indicate concentrations above which adverse effects on biota are frequently observed Long et al. (1995).

Table 2Loadings of metals on VARIMAX-rotated factors of different datasets.

Metals PC1 PC2

Pb 0.89 �0.05Cr 0.96 �0.14Ni 0.07 0.96Cu �0.36 0.92Zn �0.27 0.92Fe 0.90 �0.25Ti 0.75 �0.63Eigen value 3.28 3.08Percentage of total variance 46.92% 44.05%Cumulative percentage variance 46.92% 90.97%

Table 3Chemical fractionation of metals in surface sediments of Zhelin Bay (%).

Acid soluble Reducible Oxidizable Residual

Pb Mean, SD 2.7 ± 0.6 46.6 ± 9.9 2.2 ± 0.7 48.4 ± 10.1Median 2.8 45.2 2.4 49.2Range 1.4–3.7 33.6–68.0 0.8–3.4 25.8–62.5

Cr Mean, SD 0.7 ± 0.2 5.4 ± 1.5 6.9 ± 2.6 86.9 ± 3.2Median 0.7 5.1 6.2 87.3Range 0.4–1.1 3.3–8.2 4.1–15.3 79.9–92.1

Ni Mean, SD 2.8 ± 0.8 2.8 ± 08 3.8 ± 2.9 90.7 ± 3.1Median 2.7 2.6 2.9 91.3Range 1.8–4.0 1.5–4.1 1.9–14.2 82.0–94.5

Cu Mean, SD 0.6 ± 0.5 6.8 ± 2.3 3.4 ± 1.2 89.1 ± 3.7Median 0.7 7.0 3.2 89.5Range 0.0–1.6 2.4–10.0 1.7–6.0 83.2–95.4

Zn Mean, SD 5.7 ± 2.3 11.8 ± 5.2 7.6 ± 1.2 74.9 ± 6.4Median 5.3 11.0 7.4 75.6Range 3.5–12.9 7.4–30.3 6.0–10.1 57.6–82.6

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10

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Fig. 4. Spatial distribution of mean ERM quotient values in surface sediments ofZhelin Bay.

Y.-G. Gu et al. / Marine Pollution Bulletin 81 (2014) 256–261 259

PC1 can be regarded as a combination of natural and human com-ponents, whereas PC2 represents human sources.

Metal fractionation is an important factor affecting the potentialtoxicity and mobility of metals (Fytianos and Lourantou, 2004).Total concentrations of metals in sediments can provide valuablefundamental information for sediment quality assessment.However, such information alone is insufficient to understand the

potential mobility, bioavailability, and toxicity of metals in sedi-ments because the environmental behaviors of metals dependlargely on their specific chemical forms and binding states(Filgueiras et al., 2002). Thus, the sequential extraction techniquehas been proposed to offer information about the strength andmechanisms of metals associating with sediments (Filgueiraset al., 2002).

The chemical partitioning of Pb, Cr, Ni, Cu, and Zn in this studyis summarized in Table 3. Fig. 3 shows the speciation of metals ateach site. The predominant chemical partitioning of Pb was reduc-ible and comprises a residual fraction; similar results have been re-ported for the coastal marine sediments in Pearl River Estuary andin Singapore (Cuong and Obbard, 2006; Yang et al., 2012). Thepercentage of Pb speciation distribution follows the orderresidual > reducible > acid soluble > oxidizable. Association withhydroxides of Fe and Mn can dissipate metals, especially Pb, whichis attributed to a large adsorption surface. Under reducing condi-tions, decompositions of Fe and Mn oxides occur in the subsequentremobilization of Fe3+ and Mn4+ in aquatic systems. Significantassociations between metals and Fe and Mn oxides in sedimentshave been detected in study areas that receive leaded gasolinecombustion discharges (Filgueiras et al., 2002; Gleyzes et al.,2002). The highest percentages of Cr, Ni, Cu, and Zn have beenfound in residual fractions in surface sediments (ranging from79.9% to 92.1%, 82.0% to 94.5%, 83.2% to 95.4%, and 57.6% to

260 Y.-G. Gu et al. / Marine Pollution Bulletin 81 (2014) 256–261

82.6%, respectively). Similar results for Cr, Ni, Cu, and Zn have alsobeen reported in Pearl River Estuary and in coastal Bohai Bay,respectively (Gao and Chen, 2012; Yang et al., 2012).

Numerous sediment quality guidelines (SQGs) and different in-dexes have been developed to deal with environmental concerns,and three have been chosen to estimate the contamination extentof individual metals in the surface sediments of Zhelin Bay(Table 1).

The China National Standard for marine sediments (GB18668-2002) has classified three grades of marine sediments, in whichthe contents of Cd, Pb, Cr, Cu, and Zn are regarded as parametersapplied for classifying marine sediment quality (SEPA, 2002). Basedon this criterion, first-grade sediment is suitable for mariculture,natural reserve, endangered species reserve, and human recreationand sports; second-grade sediment can be used for general indus-try and coastal tourism; and third-grade sediment is only allowedfor harbors. Accordingly, Pb and Cr at all the sites are below thethreshold values for Class I sediment, except for the two metalsat sites S3 and S16. Cu and Zn at all sites are categorized underClass II (Table 1).

The effects range low (ERL) and the effects range median (ERM)are the main parameters for estimating the adverse biologicaleffects of metals in marine and estuary sediments (Long et al.,1995). The ERL and ERM values, which depend on numeroustoxicity tests, field studies, and altered benthic communities forsediment dwelling marine animals, are applied as guidelines forassessing the incidence of adverse biological effects of numerouspollutants, including metals, in marine sediments (Long et al.,1995). Therefore, our data were compared with ERL and ERM val-ues. As shown in Table 1, the contents of Pb and Cr at all sites werebelow the ERM value and 62.5% of sites were over ERL guideline forPb and 12.5% for Cr. Nickel (Ni) at all sites were higher than ERLvalue, and 43.8% of the sites were over the ERM value. The Cuand Zn at all sites were between ERL and ERM values, indicatingthat adverse effects on benthic organisms are frequently observed.The results suggested that Cu and Zn should be given more atten-tion due to the potential environmental risks they may pose.

The geo-accumulation index (Igeo) proposed by Müller (1979)was used to determine and define metal pollution in sedimentsby comparing current concentrations with pre-industrial levels.The Igeo is defined by the following expression:

Igeo ¼ log2Cn

1:5Bn

� �

where Cn is the measured content of the examined metal (n) insediment, Bn is the background/crust content of the metal (n), and

Table 4The geoaccumulation index (Igeo) values for metals in each site.

Igeo Pb Cr Ni Cu Zn

S1 0.08 0.90 1.39 1.88 1.26S2 �0.30 0.33 1.40 2.12 1.23S3 0.45 1.11 1.12 1.36 1.14S4 0.13 0.84 1.48 2.32 1.29S5 �0.25 0.33 1.06 2.23 1.27S6 �0.15 0.42 1.31 2.11 1.32S7 0.12 0.78 1.97 2.56 1.42S8 0.22 0.61 1.46 2.15 1.38S9 0.43 0.76 0.96 1.46 1.18S10 �0.40 0.35 0.91 1.62 1.12S11 �0.15 0.41 1.73 2.61 1.53S12 0.20 0.70 0.86 1.66 1.21S13 0.17 0.71 0.81 1.23 1.12S14 �0.11 0.63 0.89 1.26 1.08S15 0.03 0.80 0.95 1.34 1.15S16 0.36 1.20 1.17 1.10 1.01Average 0.07 0.70 1.26 1.89 1.24

factor 1.5 is the background matrix correction factor due to litho-genic effects. According to Müller (1981), the Igeo can be classifiedinto the following categories: Unpolluted: Igeo 6 0; Unpolluted tomoderately: 0 < Igeo 6 1; Moderately polluted: 1 < Igeo 6 2; Moder-ately to strongly polluted: 2 < Igeo 6 3; Strongly polluted: 3 < Igeo

6 4; Strongly to extremely: 4 < Igeo 6 5; Extremely polluted:Igeo > 5. According to above categories, the Igeo of Pb, Cr and Ni insediments from 100%, 87.5% and 37.5% of sites in this study, respec-tively, were lower than ‘unpolluted’ values and showed no signs ofcontamination (Table 4). The Igeo of Cr at 12.5% of sites, Ni at 62.5%of sites, Cu at 56.3% of sites and Zn at 100% of sties were between 1and 2, indicating moderately contaminated sediments (Table 4).Sediments from 43.7% of sites were ‘moderately to strongly’ con-taminated by Cu (Table 4).

All the SQGs and index applied above took into account individ-ual metals. Based on the fact that metals always occur in sedimentsas complex mixtures, the mean ERM quotient method has beenconducted to determine the possible biological effect of combinedtoxicant groups by computing mean quotients for a large range ofcontaminants (Carr et al., 1996; Long, 2006). The mean ERM quo-tient is calculated as the following formula:

mean ERM quotient ¼ RðCx=ERMxÞ=n

where Cx is the measured concentration of the examined compo-nent (x) in sediment, ERMx is the ERM for metal x, and n is the num-ber of metals. According to the analyses of matching chemicals andtoxicity data from over 1000 sediment samples from Florida (USA)estuaries, the mean ERM quotients of less than 0.1% have a 9% prob-ability of being toxic, quotients of 0.11–0.5 have a 21% probability,quotients of 0.51–1.5 have a 49% probability, and quotients of great-er than 1.50 have a 76% probability (Long et al., 2000). In surfacesediments of Zhelin Bay, the mean ERM quotients varied withinthe range of 0.38–0.66, and 81.25% of sites had mean ERM quotients<0.5 indicating that the combination of the five studied metals mayhave a 21% probability of being toxic, while 18.75% of sites with thequotients of >0.51 have a 49% probability of being toxic (Fig. 4).

Acknowledgements

We gratefully acknowledge the Special Scientific ResearchFunds for Central Non-profit Institutes, South China Sea FisheriesResearch Institute, Chinese Academy of Fishery Sciences(2012TS25), National Natural Science Foundation of China(41372034) and Guangdong Higher Education Institutes Grant forHigh-level Talents. The authors would like to acknowledge anony-mous reviewers for helpful comments on the manuscript.

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