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Using whole body elemental fingerprint analysis to distinguish different populations of Coilia nasus in a large river basin
Biochemical Systematics and Ecology 60 (2015) 249e257
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Biochemical Systematics and Ecology journal homepage: www.elsevier.com/locate/biochemsyseco
Using whole body elemental fingerprint analysis to distinguish different populations of Coilia nasus in a large river basin Jing Lai a, Liangjie Zhao a, Yingchun Fan a, Xiancheng Qu a, Dayong Liu b, Zhenglong Guo b, Yaohui Wang b, Qigen Liu a, *, Yushun Chen c, ** a
Key Laboratory of Freshwater Fishery Germplasm Resources, Ministry of Agriculture, Shanghai Ocean University, 201306, PR China Nantong Longyang Fisheries Co., Ltd., Jiangsu Zhongyang Group, Hai'an County, Jiangsu Province, China Institute of Hydrobiology & State Key Laboratory of Freshwater Ecology and Biotechnology, Chinese Academy of Sciences, Wuhan, Hubei 430072, China b c
a r t i c l e i n f o
a b s t r a c t
Article history: Received 10 February 2015 Accepted 19 April 2015 Available online 27 May 2015
Coilia nasus is widely distributed along the coast, and rivers and lakes that connect with the ocean in China. It is a diadromous fish but has residential populations in the middle and lower reach of the Yangtze River and its associated lakes such as Lake Taihu. These ecologically differentiated diadromous and residential populations are difficult to identify and separate, even with current molecular screening methods such as applying the mtDNA markers. The objective of the present study was to screen these populations by the means of the whole body elemental fingerprint analysis (EFA). Our results showed that the diadromous population had significantly lower concentrations of Al, B, Ca, Cr, Fe, K, Mg, Mn, P, S, and Zn, and significantly higher As than the residential populations. The spawners had lower concentrations of B, Cr, P, and S before laying eggs than after laying eggs. Advanced statistical models detected distinct spatial patterns between the diadromous and residential populations, and between groups before and after spawning. The current study showed that the whole body EFA can clearly distinguish C. nasus populations in the Yangtze River Basin and thus proved to be useful and may substitute the elemental fingerprint of fish otolith in future fish ecological studies that need to identify different populations. © 2015 Elsevier Ltd. All rights reserved.
Keywords: Coilia nasus Whole body elemental fingerprint analysis (EFA) The Yangtze River Diadromous Landlocked Population
1. Introduction Coilia nasus is an economically important fish species in China, which widely distributes along the coastal areas (e.g., the Yellow Sea, and East China Sea) and in the rivers that connect to the seas (e.g., the Yellow River and Yangtze River). This species has long been considered as a delicacy in the estuary provinces of the Yangtze River (such as Jiangsu and Shanghai) of China, thus its market price has continually going up due to the rapid decline of the natural populations in recent years (e.g., the maximum price reached over US $ 2500 per kilogram in 2012). This stimulates great research interests to the
* Corresponding author. Tel./fax: þ86 21 61900429. ** Corresponding author. E-mail addresses: [email protected] (Q. Liu), [email protected] (Y. Chen). http://dx.doi.org/10.1016/j.bse.2015.04.029 0305-1978/© 2015 Elsevier Ltd. All rights reserved.
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development of breeding technology on this fish (Xu et al., 2011a, 2011b), because lacking of fish fry or fingerling is still the bottle-neck for both commercial aquaculture and resource conservation. There are also taxonomic uncertainties that have been attracting attentions of scientific studies on this species. C. nasus is a diadromous species, which spawns in the freshwater rivers and their connected lakes, and grows up in the seas. However, the population of this species in Lake Taihu was found to reside all the life time within the lake (Yuan et al., 1976, 1980). Thus, this population is at least ecologically differentiated from the diadromous population. This landlocked or residential population of C. nasus was identified as a subspecies as Coilia ectenes taihuensis by Yuan et al. (1976). However, the validity of the subspecies was not supported by either morphological evidence (Liu, 1995) or recent molecular analyses (Cheng et al., 2006; Tang et al., 2007; Yang et al., 2008; Yang, 2012). In addition to the residential population in Lake Taihu (and perhaps in Lake Chaohu, too (Yang et al., 2010)), there is another residential population distributes in the middle reach of the Yangtze River and its connected lakes. This population was formerly identified as Coilia brachygnathus due to its shorter maxilla compared to the migrating population (Yuan et al., 1980; Whitehead et al., 1988). However, the validity of C. brachygnathus was challenged, especially from recent evidence of molecular analyses (Tang et al., 2007; Zhou et al., 2010). These studies showed that there is little differentiation between the two populations using either mtDNA or the nuclear DNA markers (Tang et al., 2007; Yang et al., 2010; Zhou et al., 2010; Yang, 2012). Thus the different populations are now all considered as taxonomically the same. They are indeed similar to each other morphologically and molecularly, however, they are ecologically differentiated and having different market values because of their flesh tastes (i.e., the taste of the diadromous population is better). Moreover, the distinction among different migration stages is not clear for the migrating population. So it is essential to develop more accurate methods for distinguishing these different populations. Elemental fingerprint analysis is proved to be a very useful technology in distinguishing biological species or populations lvez et al., 2009; Ye et al., 2011). A similar approach by using (e.g., Shinn et al., 2000; Husted et al., 2004; Chen et al., 2009; Gonza the elemental fingerprinting of fish otoliths has been traditionally applied in various fish ecological studies and especially the life history of fishes (e.g., Campana et al., 1994, 1995; Brazner et al., 2004; Jiang et al., 2012). The otolith elemental fingerprinting approach is based on the Sr: Ca ratio in the otolith of fish, and often needs complicate sample pre-treatment procedures and advanced instrument (e.g., the laser ablation ICP-MS). In addition, it needs specific skills to collect otolith from the fish, especially from those small fishes. The approach that we proposed here is a simpler one that just uses the whole fish body instead of the tiny otolith as the experimental material. The objective of this study is to distinguish different populations of C. nasus collected in the Yangtze River Basin by the means of the elemental fingerprint analysis method (whole fish body). By proposing a more convenient methodology with lower cost, our study is expected to be valuable for future fish ecological studies such as stock management or population dynamics of fish species that as ecologically complicated as C. nasus. 2. Materials and methods 2.1. Study area, fish collection and sample preparation During MarcheOctober of 2013, a total of 57 C. nasus samples (five wild populations and one cultured population) were collected from six different sites in the Yangtze River Basin: the East China Sea (Donghai or DH), Yongji harbor (YJG) in Jiangsu province, Wuhu city (WH) in Anhui province, Taihu Lake (TH) in Jiangsu province, Dongtinghu Lake (DTH) in Hunan province, and Zhongyang Group (ZY, an aquaculture facility, located in Nantong city, Jiangsu province), China (Fig. 1 and Table 1). Among
Fig. 1. Location of C. nasus sampling area in the Yangtze River Basin, China.
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Table 1 Characteristics of six C. nasus groups studied in the Yangtze River Basin, China, presented as means (standard deviation). Code
Sampling area
Collection month
N
Standard length (cm)
Wet weight (g)
DH YJG WH DTH TH ZY
The East China Sea Yongji harbor Wuhu city Dongtinghu Lake Taihu Lake Zhongyang Group
March March June August May to September October
6 6 9 9 20 7
30.47(1.52) 31.38(2.56) 29.12(1.52) 24.27(2.28) 14.96(5.79) 15.27(0.90)
130.88(37.15) 137.68(45.47) 80.16(13.98) 52.94(13.68) 15.92(12.25) 11.21(1.73)
them, the DH, YJG and WH groups were diadromous groups while the TH and DTH groups were residential populations. Based on capture times, the TH group was further divided into two sub-groups: TH-S, the Taihu Lake samples collected during the spawning season, and TH, the samples collected after spawning. All wild samples were caught by using gill nets. Once on board, the fish samples were tag marked by sampling site coordinates, and placed in plastic bags. And then the samples were frozen at 20 C for storage until processing in the laboratory. In the laboratory, all samples were first defrosted. Then standard length was measured in millimeters. Fresh weight was measured in milligrams (±0.1 mg). All samples were dissected through stainless steel scalpels, and all internal organs in the abdominal cavity were removed. Samples lost scales with varying degrees when they were caught and transported. To reduce error in analysis, residual scales of each fish were removed thoroughly. The samples were then carefully rinsed six times with MillieQ water (Millipore Corp., USA) (Ye et al., 2011). Individual samples were cut into pieces to make them dry fully. After 24 h of freeze-drying, the samples were grinded to fine powder using an organization grinding machine, and placed immediately into a desiccator prior to analysis. Each dry sample, 0.5 ± 0.005 g, was placed into a digestion tube, and 10 ml of purified HNO3 (MOS reagent, Sinopharm Chemical Reagent Co., Ltd., China) was added and still stood for three hours. To digest lipids completely, a 2 ml of purified HClO4 was added into each tube. Then, all the samples were digested using an electric thermal plate, and the clearly digested samples were then transferred quantitatively to 100 ml calibrated flasks with the Milli-Q water.
2.2. Elemental analysis An Inductively Coupled Plasma Optical Emission Spectrometer (ICP-OES, iCAP6300, Thermo Jarrell Ash Corp., Franklin, MA, USA) was used to measure concentrations of a total of 27 chemical elements in the pre-digested samples: Al (Aluminum), As (Arsenic), B (Boron), Ca (Calcium), Cd (Cadmium), Co (Cobalt), Cr (Chromium), Cu (Copper), Fe (Iron), K (Potassium), Li (Lithium), Mg (Magnesium), Mn (Manganese), Mo (Molybdenum), Na (Sodium), Ni (Nickel), P (Phosphorus), Pb (Lead), S (Sulfur), Se (Selenium), Si (Silicon), Sn (Tin), Sr (Strontium), Ti (Titanium), V (Vanadium), W (Tungsten), and Zn (Zinc). The calibration standards were prepared from a multi-elemental mixed calibration standard stock solution (Al, As, B, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, P, Pb, Se, V, Zn, Lot#:10-95CR; Cat#:XCCC-14B-500, and S, Si, Sn, Sr, Ti, W, Lot#:2-241XL; Cat#:XCCC-13A-500, SPEX CertiPrep Group, USA). All these analyses were replicated three times. The elements Cd, Co, Li, and Mo were excluded because they were not detected in all samples.
2.3. Statistical analyses Data were expressed on a dry weight (DW) basis. Element concentrations in fish groups were subjected to one-way analysis of variance (ANOVA) to test difference among groups for each element at a significance level of a ¼ 0.05. Statistical analyses were performed using the program SPSS V19.0 (SPSS Inc., USA). Then, a couple of advanced statistical models were used to detect spatial patterns of these fish groups. First, principal components analysis (PCA) was applied to detect the overall variation pattern of the elements, and the correlations of the six groups with the principal components (Quinn and Keough, 2002; Legendre and Legendre, 2012). Then, hierarchical cluster analysis (HCA) was performed using selected chemical descriptors as variables, with the squared Euclidean distance as similarity measurement and Ward's method as amalgamation rule (Quinn and Keough, 2002; Legendre and Legendre, 2012). Finally, to find an operative classification rule for discriminating fish groups, a supervised learning pattern recognition technique, stepwise linear discriminant analysis (LDA) was used to complement to PCA. The LDA was applied to raw data by using stepwise modes to evaluate differences in groups. The variables included in the analysis are determined with a stepwise LDA, using a Wilk's lambda selection criterion and an F-statistic factor, to establish the significance of changes in lambda when a new variable is tested (Quinn and Keough, 2002; Legendre and Legendre, 2012). The stepwise LDA was applied to develop a set of discriminating functions, which were derived from values of the 17 quantitative variables (i.e., Mg, S, Ca, P, K, Zn, Mn, As, B, Cr, Cu, Si, Al, Sr, Ti, V and Se in fish) that are capable of distinguishing fish groups.
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Table 2 Comparisons of elements concentrations (mg/kg) in C. nasus from six groups in the Yangtze River Basin, China, presented as means (standard error). Group DH
YJG
WH
TH-S
TH
DTH
ZY
Al As B Ca Cr Cu Fe K Mg Mn Na Ni P Pb S Se Si Sn Sr Ti V W Zn
13.25(0.96)a 15.00(1.41)a 1.83(0.75)a 11482.00(1154.80)a 1.00(0.01)a 11.20(1.64)a 42.75(5.25)ab 4051.60(285.98)a 603.33(17.16)a 2.67(0.52)a 2179.80(381.71)abcd 6.75(3.20)abc 8370.33(186.43)a 1.67(0.82)a 3838.67(362.84)a 2.17(1.17) 35.80(5.85)a 1.00(0.01) 30.00(5.85)ab 0.00(0.00)a 0.00(0.00)a 1.00 (0.01)a 25.00.(3.39)a
23.75(4.57)bc 14.80(4.97)a 1.50(0.58)a 12342.50(1150.23)a 1.00(0.01)a 13.17(1.83)a 37.00(3.16)a 5218.33(221.85)b 572.50(41.32)a 3.83(0.75)a 1166.75(98.13)e 1.83(0.40)abd 8427.00(422.40)a 1.33(0.52)a 3996.50(371.61)a 1.80(0.84) 25.50(3.79)ab 1.00(0.01) 38.00(1.83)a 0.00(0.00)a 0.00(0.00)a 1.25(0.50)ab 24.25(2.06)a
21.33(2.08)b 34.00(7.98)b 1.00(0.01)a 26670(1178.49)b 3.86(2.67)ab 12.80(0.45)a 47.50(6.66)b 6042.86(498.07)b 904.50(25.04)b 6.38(1.41)b 1766.00(86.47)a 3.67(2.66)abc 15576.67(90.73)b 4.75(1.70)b 6304.80(349.19)b 2.22(1.39) 32.17(9.06)ab 1.02(0.01) 58.00(5.34)c 2.50(1.29)b 1.33(0.58)b 1.67(0.58)ab 43.00(3.35)b
35.00(2.65)d 1.67(1.15)c 1.00(0.02)a 35323.33(1210.67)ce 1.67(0.58)a 33.00(3.61)b* 104.67(1.15)c 11146.67(45.09)cd 1371.50(48.99)c 15.00(2.94)cd 2504.00(21.28)b 7.00(1.00)c 22546.67(453.69)c 1.33(0.58)a 9195.00(456.47)c 2.50(0.58) 88.00(9.54)c 1.00(0.01) 40.50(3.42)ab 1.33(0.58)b 0.00(0.00)a 1.00(0.01)a 86.00(2.16)c
24.67(4.50)bc 3.00(1.41)c 15.40(4.56)b 40405(277.43)cd 9.62(1.41)c 2.87(1.25)c 77.75(4.06)d 10188.40(567.22)de 1405.60(78.16)cd 12.69(2.69)c 2719.33(54.51)c 2.06(1.00)abd 26456.00(161.64)d 1.75(0.62)a 10213.25(333.79)d 3.20(1.87) 11.88(1.64)d 1.14(0.90) 39.00(2.00)a 1.40(0.84)b 1.56(0.73)b 2.62(1.45)b 93.00(6.08)cd
49.25(5.25)e 3.25(0.96)c 23.63(6.70)b 36934.00(617.68)ef 6.00(1.00)b 2.67(1.12)c 101.25(2.22)ce 9618.00(298.58)e 1467.17(46.21)d 12.22(2.73)c 2304.60(65.93)d 1.20(0.45)ad 25973(731.19)d 2.00(0.82)a 9570.17(323.20)c 1.80(0.84) 18.00(5.10))bd 1.00(0.01) 52.85(6.77)c 2.00(1.53)b 2.00(1.41)b 2.40(1.14)ab 72.33(6.28)e
29.25(5.12)cd 2.60(1.67)c 21.00(1.41)b 36732.50(693.13)ef 11.83(2.32)c 2.57(0.79)c 96.33(11.06)e 10355.00(281.13)de 1332.60(42.17)c 16.60(1.14)d 3352.00(71.76)f 2.57(0.79)b 22748.00(697.98)c 2.14(0.69)a 8429.00(344.81)e 1.25(0.50) 11.00(2.12)d 1.00(0.01) 33.00(1.00)b 1.50(1.00)b 1.00(0.01)b 2.50(0.58)ab 104.00(6.78)d
Note: Fish groups: DH ¼ Donghai or East China Sea, YJG ¼ Yongji harbor, WH ¼ Wuhu city, TH-S ¼ Taihu Lake samples during spawn, TH ¼ Taihu Lake samples after spawn, DTH ¼ Dongtinghu Lake, and ZY ¼ Zhongyang group; Means followed by different letters represent significant difference (P < 0.05) among different groups. *Extremely high values at TH-S with unknown reasons.
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Element
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3. Results 3.1. Difference in element concentrations among groups Among the minerals and essential trace elements, Ca, K, Mg, Na, P and S were present at higher levels (i.e., >1000 mg/kg in most samples), followed by Al, Fe, Si, Sr and Zn that were at moderate levels, and then As, B, Cr, Cu, Mn, Ni, Pb, Se, Sn, Ti, V and W were at lower levels (Table 2). Overall, elements of Al, B, Ca, Cr, Fe, K, Mg, Mn, P, S, and Zn were significantly lower in the diadromous populations than those in the residential populations (Table 2). The concentrations of As and Cu (except TH-S) were significantly higher in the diadromous populations than those in the residential populations (Table 2). In general, elements of B, Cr, P, and S were lower in spawners before laying eggs than after laying eggs in the TH residential population (Table 2). The concentrations of Al and Fe were higher in spawners before laying eggs than after laying eggs in the TH residential population (Table 2). 3.2. Group patterns based on element concentrations The first two principal components (PC 1 & 2) explained 50.2% of the total variance. Differences observed in the element pattern of the six groups were primarily due to the characteristic element fingerprint of Mg, S, Ca, P, K, Zn, Mn, As, B, Cr, Cu, Si, Al, Sr, Ti, V and Se that presented for each group (Fig. 2). Diadromous DH, YJG and WH groups were associated with positive PC 2 and negative PC 1 scores whereas the residential groups were associated with both positive PC 1 and PC 2 scores except the TH-S group (Fig. 2). Similar patterns were obtained in a dendrogram through the HCA (Fig. 3). The original data matrix was divided into four main clusters, which showed a clear discrimination between the diadromous and residential groups (Fig. 3). In diadromous groups, the DH and YJG were classified into one group, and WH was classified as the other. In the residential groups, the TH-S and the other three (TH, ZY and DTH) were classified as the rest two main clusters (Fig. 3). The classification of the two populations (diadromous and residential) was 100% accurate in total based on the discriminant functions 1 and 2 plot (Fig. 4). The diadromous populations were clustered at the lower left while the residential populations were clustered at the upper right (Fig. 4). 4. Discussion The elemental contents of a fish depends on several factors, including fish species, food resources, and environmental conditions. In our study, the differences between the two populations, diadromous and residential populations, of C. nasus
Fig. 2. Principal Components Analysis (PCA) plot of six C. nanus groups in the Yangtze River Basin, China.
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Fig. 3. Hierarchical Cluster Analysis (HCA) dendrogram of six C. nanus groups in the Yangtze River Basin, China.
were striking. This may be related to the difference of environmental conditions (e.g. salinity, temperature, mineral composition, and pH) where these populations primarily experienced. For example, the Chinese mitten crabs (Eriocheir sinensis) from different lakes (Taihu Lake, Shijiu Lake and Gucheng Lake) in Jiangsu Province were consistently identified because of the environmental difference among these lakes (Yang et al., 2012). The results from PCA, HCA and LDA showed that the ZY group was classified as the residential population. In the YZ group, juvenile diadromous population of C. nasus
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Fig. 4. Linear Discriminant Analysis (LDA) plot of six C. nanus groups in the Yangtze River Basin, China.
were wild caught at the Yangtze estuary while artificially fed in ponds with fresh water. This further supported that the primary reason of the population disparity was most likely due to the difference of primary growing environmental conditions. The diadromous populations did not feed until they laid eggs and back to the sea Therefore, the differences of element contents in fish body were mainly attributed to environment variations and their physiological costs (e.g. sustaining life, migration and spawning). From a physiological point, Ca, K, Mg, and Na concentrations of fishes in the sea should be higher than those in freshwater due to their roles in regulating osmotic pressure. However, the concentrations of these elements in those of the sea groups, like DH group, actually showed lower than the others. This may be caused by the difference of the Fullness of the fish samples. The Fullness was calculated from wet weight (W) and standard length (L) at Fullness ¼ (W/L3) *100. We found that the Fullness showed a gradual decrease from DH to WH as our samples moved towards the inland, with 0.45 (0.03), 0.43 (0.02) and 0.32 (0.01), respectively. This is because they not only migrate extensively but also complete the development of gonad (Guan et al. 2010). When a fish has a lower level of Fullness, it has fewer lipids in its muscle and viscera. The concentrations of the elements will decrease when body lipid content decreases. Elemental fingerprint analysis has long been used in fish ecological studies, especially in distinguishing fish populations or stocks (Campana et al., 1994, 1995, 2000; Brazner et al., 2004). However, the traditional elemental fingerprints approach in fish ecological studies primarily used the elemental composition of fish otoliths, rather than the other tissue or even the whole body. This method is based on two aspects: (1) otoliths grow throughout the life of the fish, and unlike bones, are metabolically inert, therefore, newly deposited material is neither resorbed nor reworked after deposition (Campana and Neilson, 1985), and (2) the calcium carbonate and trace element that make up 90% of otolith appear to be mainly derived from the water (Simkiss, 1974). Accordingly, the elemental chemicals uptake onto the growing otolith may reflect the physical and chemical environment the fish live (Fowler et al., 1995; Gallahar and Kingsford, 1996), albeit with significant physiological regulation (Kalish, 1989; Farrell and Campana, 1996). So this approach has drawn many interests in recent decades (Campana et al., 2000). However, this traditional approach of elemental fingerprints has some obvious shortcomings in application: (1) otolith of fish is not always easily available, or big enough to be prepared for further measurement upon available, especially for those of small sized or at young age or earlier life stage; (2) measuring the chemicals in otoliths is not an easy task, it needs complicated pre-treatment such as the laser ablation, which might limit its application; (3) since otolith is metabolically inert and small in size, it may not be so sensitive as to be able to reflect any change of habitats that is as less significant as the shifts between freshwater and marine environments, and (4) since fish otoliths do not involve the metabolism of the fish, this approach can only be used to trace the environment change of fish, it can not reflect the ecological interactions between fish and its environment. The information that the Sr: Ca ratio of the fish otolith can convey would be very limited, though it is a good indicator for the shift between freshwater and marine environments.
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Compared to this traditional approach, the current approach we used here analyzed the whole body chemical composition, which is usually more informative than those of otoliths of the fish. Because the chemicals of fish body are actively involving the metabolism, their composition is resulted from the interactions between the fish and environment. That is, EFA can tightly link to ecological stoichiometry, and thus can reveal more information than otolith elemental fingerprints in understanding of the interactions between fish and habitat. Our current results demonstrated that using whole body EFA could not only clearly distinguish the migratory population of Coilia nasus from the residential, but also the spawners from the non-spawners. EFA of either part or the whole body of an organism has traditionally or most often been used in identifying the localities of certain agricultural products such as teas, barley and wines, etc. (Marcos et al., 1998; Hill, 1998; Husted et al., 2004; Gonzalve et al., 2009). It has even been successfully used to identify the differentiation between cultivars of tea planted in the same plantation (Chen et al., 2009). Our current results indicated that this approach could also be used in animal ecological study such as fish ecology with satisfactory results. This might be because: (1) the food sources of the fish are often more locally distributed, and could better reflect the tributes of the environment. So even if the fish might be a migratory species or a newly inhabitator to a certain habitat, when it moves to a new environment, it will be imprinted with this new environment through the feeding on the local food; (2) the chemical composition in fish food and water can also affect the fish body chemical compositions through either osmoregulation or active absorption of the fish. Since the chemicals of the fish body are all actively involved in the metabolism, they are more easily changed by new environment than those of the fish otoliths, thus are more sensitive indicators of habitat shifts. For example, from our current results, we could even distinguish samples of the migratory C. nasus between the YJG and WH groups in the Yangtze River. It will be difficult to distinguish the two groups if fish otoliths were used, because it will not take a long time (usually within months) for the fish to migrate from the Yongji Harbor (YJG) to Wuhu City (WH) for spawning where the chemical composition of the otoliths may not change substantially. As a summary, C. nasus is a taxonomically controversial species in China, with many different eco-types of populations, and it is usually difficult to distinguish them from each other either morphologically or molecularly. Our results showed that these populations, even for the different migratory stages of the diadromous populations, can be distinguished using the whole body chemical composition. The whole body EFA may be applicable in other fish ecological studies as well. Acknowledgments We thank all the personals who have provided a lot of assistance during the field sampling and lab analysis. 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Husted, S., Mikkelsen, B.F., Jensen, J., Nielsen, N.E., 2004. Elemental fingerprint analysis of barley (Hordeum vulgare) using inductively coupled plasma mass spectrometry, isotope-ratio mass spectrometry, and multivariate statistics. Anal. Bioanal. Chem. 378 (1), 171e182. Jiang, T., Yang, J., Liu, H., Shen, X.Q., 2012. Life history of Coilia nasus from the Yellow Sea inferred from otolith Sr: Ca ratios. Environ. Biol. Fish. 95 (4), 503e508. Kalish, J.M., 1989. Otolith microchemistry: validation of the effects of physiology, age and environment on otolith composition. J. Exp. Mar. Biol. Ecol. 132 (3), 151e178. Legendre, P., Legendre, L.F., 2012. Numerical Ecology, vol. 20. Elsevier. Liu, W., 1995. Biochemical and morphological comparison and interspecific relationships of four species of the genus Coilia in China. Oceanol. Limnol. Sin. 26, 565e568 (in Chinese with English abstract). Marcos, A., Fisher, A., Rea, G., et al., 1998. Preliminary study using trace element concentrations and a chemometrics approach to determine the geographical origin of tea. J. Anal. At. Spectrom. 13 (6), 521e525. Quinn, G.P., Keough, M.J., 2002. Experimental Design and Data Analysis for Biologists. Cambridge University Press.
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Shinn, A., Bron, J., Gray, D., Sommerville, C., 2000. Elemental analysis of Scottish populations of the ectoparasitic copepod Lepeophtheirus salmonis. Contrib. Zool. 69 (1/2), 79e87. Simkiss, K., 1974. Calcium metabolism of fish in relation to ageing. In: Bagenal, T.B. (Ed.), The Ageing of Fish. Unwin Brothers Ltd, Surrey, England, pp. 1e12. Tang, W., Hu, X., Yang, J., 2007. Species validities of Coilia brachygnathus and C. nasus taihuensis based on sequence variations of complete mtDNA control region. Biodivers. Sci. 15, 224e231 (in Chinese with English abstract). Whitehead, P.J.P., Nelson, G.J., Wongratana, T., 1988. FAO species catalogue. v. 7: Clupeoid fishes of the world (Suborder Clupeoidei). An annotated and illustrated catalogue of the herrings, sardines, pilchards, sprats, shads, anchovies and wolf-herrings. pt. 2: Engraulididae. FAO Fish. Synop. (FAO) 125, 305e579. Xu, G., Wan, J., Gu, R., Zhang, C., Xu, P., 2011a. Morphological and histological studies on ovary development of Coilia nasus under artificial farming conditions. J. Fish. Sci. China 18 (3), 537e546 (in Chinese with English abstract). Xu, G., Tang, X., Zhang, C., Gu, R., Zheng, J., 2011b. First studies of embryonic and larval development of Coilia nasus (Engraulidae) under controlled conditions. Aquac. Res. 42 (4), 593e601. Yang, Q., 2012. Phylogentic Analysis of Genus Coilia in China and Molecular Phylogeography of C. nasus and C. mystus. PhD Thesis. Ocean University of China (in Chinese). Yang, J., Hu, X., Tang, W., Lin, H., 2008. mtDNA control region sequence variation and genetic diversity of Coilia nasus in Yangtze River estuary and its adjacent waters. Chin. J. Zool. 43 (1), 8e15 (in Chinese with English abstract). Yang, Q., Han, Z., Sun, D., Xie, S., Lin, L., Gao, T., 2010. Genetics and phylogeny of genus Coilia in China based on AFLP markers. Chin. J. Oceanol. Limnol. 28 (4), 795e801. Yang, W., Su, Y., Liu, H., Yang, J., Xu, P., 2012. A comparative study of morphological characteristics and elemental fingerprints of Chinese mitten crab Eriocheir sinensisfrom in three lakes. J. Fish. Sci. China 19 (1), 84e93 (in Chinese with English abstract). Ye, S., Yang, J., Liu, H., Oshima, Y., 2011. Use of elemental fingerprint analysis to identify localities of collection for the large icefish Protosalanx chinensis in Taihu Lake, China. J. Fac. Agr 56 (1), 41e45. Yuan, C., Lin, J., Qing, A., Liu, R., 1976. On the classification history and status quo of genus Coilia in China. J. Nanjing Univ. 2, 1e12 (in Chinese with English abstract). Yuan, C., Qing, A., Liu, R., Lin, J., 1980. Discussion on subspeciefic taxanomy of the genus Coilia in middle and lower reaches of Changjiang and southeast coastal China Sea. J. Nanjing Univ. 3, 67e82 (in Chinese with English abstract). Zhou, X.D., Yang, J.Q., Tang, W.Q., Liu, D., 2010. Species validities analyses of Chinese Coilia fishes based on mtDNA CO I barcoding. Acta Zootax. Sin. 35 (4), 819e826 (in Chinese with English abstract).
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Using whole body elemental fingerprint analysis to distinguish different populations of Coilia nasus in a large river basin Jing Lai a, Liangjie Zhao a, Yingchun Fan a, Xiancheng Qu a, Dayong Liu b, Zhenglong Guo b, Yaohui Wang b, Qigen Liu a, *, Yushun Chen c, ** a
Key Laboratory of Freshwater Fishery Germplasm Resources, Ministry of Agriculture, Shanghai Ocean University, 201306, PR China Nantong Longyang Fisheries Co., Ltd., Jiangsu Zhongyang Group, Hai'an County, Jiangsu Province, China Institute of Hydrobiology & State Key Laboratory of Freshwater Ecology and Biotechnology, Chinese Academy of Sciences, Wuhan, Hubei 430072, China b c
a r t i c l e i n f o
a b s t r a c t
Article history: Received 10 February 2015 Accepted 19 April 2015 Available online 27 May 2015
Coilia nasus is widely distributed along the coast, and rivers and lakes that connect with the ocean in China. It is a diadromous fish but has residential populations in the middle and lower reach of the Yangtze River and its associated lakes such as Lake Taihu. These ecologically differentiated diadromous and residential populations are difficult to identify and separate, even with current molecular screening methods such as applying the mtDNA markers. The objective of the present study was to screen these populations by the means of the whole body elemental fingerprint analysis (EFA). Our results showed that the diadromous population had significantly lower concentrations of Al, B, Ca, Cr, Fe, K, Mg, Mn, P, S, and Zn, and significantly higher As than the residential populations. The spawners had lower concentrations of B, Cr, P, and S before laying eggs than after laying eggs. Advanced statistical models detected distinct spatial patterns between the diadromous and residential populations, and between groups before and after spawning. The current study showed that the whole body EFA can clearly distinguish C. nasus populations in the Yangtze River Basin and thus proved to be useful and may substitute the elemental fingerprint of fish otolith in future fish ecological studies that need to identify different populations. © 2015 Elsevier Ltd. All rights reserved.
Keywords: Coilia nasus Whole body elemental fingerprint analysis (EFA) The Yangtze River Diadromous Landlocked Population
1. Introduction Coilia nasus is an economically important fish species in China, which widely distributes along the coastal areas (e.g., the Yellow Sea, and East China Sea) and in the rivers that connect to the seas (e.g., the Yellow River and Yangtze River). This species has long been considered as a delicacy in the estuary provinces of the Yangtze River (such as Jiangsu and Shanghai) of China, thus its market price has continually going up due to the rapid decline of the natural populations in recent years (e.g., the maximum price reached over US $ 2500 per kilogram in 2012). This stimulates great research interests to the
* Corresponding author. Tel./fax: þ86 21 61900429. ** Corresponding author. E-mail addresses: [email protected] (Q. Liu), [email protected] (Y. Chen). http://dx.doi.org/10.1016/j.bse.2015.04.029 0305-1978/© 2015 Elsevier Ltd. All rights reserved.
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development of breeding technology on this fish (Xu et al., 2011a, 2011b), because lacking of fish fry or fingerling is still the bottle-neck for both commercial aquaculture and resource conservation. There are also taxonomic uncertainties that have been attracting attentions of scientific studies on this species. C. nasus is a diadromous species, which spawns in the freshwater rivers and their connected lakes, and grows up in the seas. However, the population of this species in Lake Taihu was found to reside all the life time within the lake (Yuan et al., 1976, 1980). Thus, this population is at least ecologically differentiated from the diadromous population. This landlocked or residential population of C. nasus was identified as a subspecies as Coilia ectenes taihuensis by Yuan et al. (1976). However, the validity of the subspecies was not supported by either morphological evidence (Liu, 1995) or recent molecular analyses (Cheng et al., 2006; Tang et al., 2007; Yang et al., 2008; Yang, 2012). In addition to the residential population in Lake Taihu (and perhaps in Lake Chaohu, too (Yang et al., 2010)), there is another residential population distributes in the middle reach of the Yangtze River and its connected lakes. This population was formerly identified as Coilia brachygnathus due to its shorter maxilla compared to the migrating population (Yuan et al., 1980; Whitehead et al., 1988). However, the validity of C. brachygnathus was challenged, especially from recent evidence of molecular analyses (Tang et al., 2007; Zhou et al., 2010). These studies showed that there is little differentiation between the two populations using either mtDNA or the nuclear DNA markers (Tang et al., 2007; Yang et al., 2010; Zhou et al., 2010; Yang, 2012). Thus the different populations are now all considered as taxonomically the same. They are indeed similar to each other morphologically and molecularly, however, they are ecologically differentiated and having different market values because of their flesh tastes (i.e., the taste of the diadromous population is better). Moreover, the distinction among different migration stages is not clear for the migrating population. So it is essential to develop more accurate methods for distinguishing these different populations. Elemental fingerprint analysis is proved to be a very useful technology in distinguishing biological species or populations lvez et al., 2009; Ye et al., 2011). A similar approach by using (e.g., Shinn et al., 2000; Husted et al., 2004; Chen et al., 2009; Gonza the elemental fingerprinting of fish otoliths has been traditionally applied in various fish ecological studies and especially the life history of fishes (e.g., Campana et al., 1994, 1995; Brazner et al., 2004; Jiang et al., 2012). The otolith elemental fingerprinting approach is based on the Sr: Ca ratio in the otolith of fish, and often needs complicate sample pre-treatment procedures and advanced instrument (e.g., the laser ablation ICP-MS). In addition, it needs specific skills to collect otolith from the fish, especially from those small fishes. The approach that we proposed here is a simpler one that just uses the whole fish body instead of the tiny otolith as the experimental material. The objective of this study is to distinguish different populations of C. nasus collected in the Yangtze River Basin by the means of the elemental fingerprint analysis method (whole fish body). By proposing a more convenient methodology with lower cost, our study is expected to be valuable for future fish ecological studies such as stock management or population dynamics of fish species that as ecologically complicated as C. nasus. 2. Materials and methods 2.1. Study area, fish collection and sample preparation During MarcheOctober of 2013, a total of 57 C. nasus samples (five wild populations and one cultured population) were collected from six different sites in the Yangtze River Basin: the East China Sea (Donghai or DH), Yongji harbor (YJG) in Jiangsu province, Wuhu city (WH) in Anhui province, Taihu Lake (TH) in Jiangsu province, Dongtinghu Lake (DTH) in Hunan province, and Zhongyang Group (ZY, an aquaculture facility, located in Nantong city, Jiangsu province), China (Fig. 1 and Table 1). Among
Fig. 1. Location of C. nasus sampling area in the Yangtze River Basin, China.
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Table 1 Characteristics of six C. nasus groups studied in the Yangtze River Basin, China, presented as means (standard deviation). Code
Sampling area
Collection month
N
Standard length (cm)
Wet weight (g)
DH YJG WH DTH TH ZY
The East China Sea Yongji harbor Wuhu city Dongtinghu Lake Taihu Lake Zhongyang Group
March March June August May to September October
6 6 9 9 20 7
30.47(1.52) 31.38(2.56) 29.12(1.52) 24.27(2.28) 14.96(5.79) 15.27(0.90)
130.88(37.15) 137.68(45.47) 80.16(13.98) 52.94(13.68) 15.92(12.25) 11.21(1.73)
them, the DH, YJG and WH groups were diadromous groups while the TH and DTH groups were residential populations. Based on capture times, the TH group was further divided into two sub-groups: TH-S, the Taihu Lake samples collected during the spawning season, and TH, the samples collected after spawning. All wild samples were caught by using gill nets. Once on board, the fish samples were tag marked by sampling site coordinates, and placed in plastic bags. And then the samples were frozen at 20 C for storage until processing in the laboratory. In the laboratory, all samples were first defrosted. Then standard length was measured in millimeters. Fresh weight was measured in milligrams (±0.1 mg). All samples were dissected through stainless steel scalpels, and all internal organs in the abdominal cavity were removed. Samples lost scales with varying degrees when they were caught and transported. To reduce error in analysis, residual scales of each fish were removed thoroughly. The samples were then carefully rinsed six times with MillieQ water (Millipore Corp., USA) (Ye et al., 2011). Individual samples were cut into pieces to make them dry fully. After 24 h of freeze-drying, the samples were grinded to fine powder using an organization grinding machine, and placed immediately into a desiccator prior to analysis. Each dry sample, 0.5 ± 0.005 g, was placed into a digestion tube, and 10 ml of purified HNO3 (MOS reagent, Sinopharm Chemical Reagent Co., Ltd., China) was added and still stood for three hours. To digest lipids completely, a 2 ml of purified HClO4 was added into each tube. Then, all the samples were digested using an electric thermal plate, and the clearly digested samples were then transferred quantitatively to 100 ml calibrated flasks with the Milli-Q water.
2.2. Elemental analysis An Inductively Coupled Plasma Optical Emission Spectrometer (ICP-OES, iCAP6300, Thermo Jarrell Ash Corp., Franklin, MA, USA) was used to measure concentrations of a total of 27 chemical elements in the pre-digested samples: Al (Aluminum), As (Arsenic), B (Boron), Ca (Calcium), Cd (Cadmium), Co (Cobalt), Cr (Chromium), Cu (Copper), Fe (Iron), K (Potassium), Li (Lithium), Mg (Magnesium), Mn (Manganese), Mo (Molybdenum), Na (Sodium), Ni (Nickel), P (Phosphorus), Pb (Lead), S (Sulfur), Se (Selenium), Si (Silicon), Sn (Tin), Sr (Strontium), Ti (Titanium), V (Vanadium), W (Tungsten), and Zn (Zinc). The calibration standards were prepared from a multi-elemental mixed calibration standard stock solution (Al, As, B, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, P, Pb, Se, V, Zn, Lot#:10-95CR; Cat#:XCCC-14B-500, and S, Si, Sn, Sr, Ti, W, Lot#:2-241XL; Cat#:XCCC-13A-500, SPEX CertiPrep Group, USA). All these analyses were replicated three times. The elements Cd, Co, Li, and Mo were excluded because they were not detected in all samples.
2.3. Statistical analyses Data were expressed on a dry weight (DW) basis. Element concentrations in fish groups were subjected to one-way analysis of variance (ANOVA) to test difference among groups for each element at a significance level of a ¼ 0.05. Statistical analyses were performed using the program SPSS V19.0 (SPSS Inc., USA). Then, a couple of advanced statistical models were used to detect spatial patterns of these fish groups. First, principal components analysis (PCA) was applied to detect the overall variation pattern of the elements, and the correlations of the six groups with the principal components (Quinn and Keough, 2002; Legendre and Legendre, 2012). Then, hierarchical cluster analysis (HCA) was performed using selected chemical descriptors as variables, with the squared Euclidean distance as similarity measurement and Ward's method as amalgamation rule (Quinn and Keough, 2002; Legendre and Legendre, 2012). Finally, to find an operative classification rule for discriminating fish groups, a supervised learning pattern recognition technique, stepwise linear discriminant analysis (LDA) was used to complement to PCA. The LDA was applied to raw data by using stepwise modes to evaluate differences in groups. The variables included in the analysis are determined with a stepwise LDA, using a Wilk's lambda selection criterion and an F-statistic factor, to establish the significance of changes in lambda when a new variable is tested (Quinn and Keough, 2002; Legendre and Legendre, 2012). The stepwise LDA was applied to develop a set of discriminating functions, which were derived from values of the 17 quantitative variables (i.e., Mg, S, Ca, P, K, Zn, Mn, As, B, Cr, Cu, Si, Al, Sr, Ti, V and Se in fish) that are capable of distinguishing fish groups.
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Table 2 Comparisons of elements concentrations (mg/kg) in C. nasus from six groups in the Yangtze River Basin, China, presented as means (standard error). Group DH
YJG
WH
TH-S
TH
DTH
ZY
Al As B Ca Cr Cu Fe K Mg Mn Na Ni P Pb S Se Si Sn Sr Ti V W Zn
13.25(0.96)a 15.00(1.41)a 1.83(0.75)a 11482.00(1154.80)a 1.00(0.01)a 11.20(1.64)a 42.75(5.25)ab 4051.60(285.98)a 603.33(17.16)a 2.67(0.52)a 2179.80(381.71)abcd 6.75(3.20)abc 8370.33(186.43)a 1.67(0.82)a 3838.67(362.84)a 2.17(1.17) 35.80(5.85)a 1.00(0.01) 30.00(5.85)ab 0.00(0.00)a 0.00(0.00)a 1.00 (0.01)a 25.00.(3.39)a
23.75(4.57)bc 14.80(4.97)a 1.50(0.58)a 12342.50(1150.23)a 1.00(0.01)a 13.17(1.83)a 37.00(3.16)a 5218.33(221.85)b 572.50(41.32)a 3.83(0.75)a 1166.75(98.13)e 1.83(0.40)abd 8427.00(422.40)a 1.33(0.52)a 3996.50(371.61)a 1.80(0.84) 25.50(3.79)ab 1.00(0.01) 38.00(1.83)a 0.00(0.00)a 0.00(0.00)a 1.25(0.50)ab 24.25(2.06)a
21.33(2.08)b 34.00(7.98)b 1.00(0.01)a 26670(1178.49)b 3.86(2.67)ab 12.80(0.45)a 47.50(6.66)b 6042.86(498.07)b 904.50(25.04)b 6.38(1.41)b 1766.00(86.47)a 3.67(2.66)abc 15576.67(90.73)b 4.75(1.70)b 6304.80(349.19)b 2.22(1.39) 32.17(9.06)ab 1.02(0.01) 58.00(5.34)c 2.50(1.29)b 1.33(0.58)b 1.67(0.58)ab 43.00(3.35)b
35.00(2.65)d 1.67(1.15)c 1.00(0.02)a 35323.33(1210.67)ce 1.67(0.58)a 33.00(3.61)b* 104.67(1.15)c 11146.67(45.09)cd 1371.50(48.99)c 15.00(2.94)cd 2504.00(21.28)b 7.00(1.00)c 22546.67(453.69)c 1.33(0.58)a 9195.00(456.47)c 2.50(0.58) 88.00(9.54)c 1.00(0.01) 40.50(3.42)ab 1.33(0.58)b 0.00(0.00)a 1.00(0.01)a 86.00(2.16)c
24.67(4.50)bc 3.00(1.41)c 15.40(4.56)b 40405(277.43)cd 9.62(1.41)c 2.87(1.25)c 77.75(4.06)d 10188.40(567.22)de 1405.60(78.16)cd 12.69(2.69)c 2719.33(54.51)c 2.06(1.00)abd 26456.00(161.64)d 1.75(0.62)a 10213.25(333.79)d 3.20(1.87) 11.88(1.64)d 1.14(0.90) 39.00(2.00)a 1.40(0.84)b 1.56(0.73)b 2.62(1.45)b 93.00(6.08)cd
49.25(5.25)e 3.25(0.96)c 23.63(6.70)b 36934.00(617.68)ef 6.00(1.00)b 2.67(1.12)c 101.25(2.22)ce 9618.00(298.58)e 1467.17(46.21)d 12.22(2.73)c 2304.60(65.93)d 1.20(0.45)ad 25973(731.19)d 2.00(0.82)a 9570.17(323.20)c 1.80(0.84) 18.00(5.10))bd 1.00(0.01) 52.85(6.77)c 2.00(1.53)b 2.00(1.41)b 2.40(1.14)ab 72.33(6.28)e
29.25(5.12)cd 2.60(1.67)c 21.00(1.41)b 36732.50(693.13)ef 11.83(2.32)c 2.57(0.79)c 96.33(11.06)e 10355.00(281.13)de 1332.60(42.17)c 16.60(1.14)d 3352.00(71.76)f 2.57(0.79)b 22748.00(697.98)c 2.14(0.69)a 8429.00(344.81)e 1.25(0.50) 11.00(2.12)d 1.00(0.01) 33.00(1.00)b 1.50(1.00)b 1.00(0.01)b 2.50(0.58)ab 104.00(6.78)d
Note: Fish groups: DH ¼ Donghai or East China Sea, YJG ¼ Yongji harbor, WH ¼ Wuhu city, TH-S ¼ Taihu Lake samples during spawn, TH ¼ Taihu Lake samples after spawn, DTH ¼ Dongtinghu Lake, and ZY ¼ Zhongyang group; Means followed by different letters represent significant difference (P < 0.05) among different groups. *Extremely high values at TH-S with unknown reasons.
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3. Results 3.1. Difference in element concentrations among groups Among the minerals and essential trace elements, Ca, K, Mg, Na, P and S were present at higher levels (i.e., >1000 mg/kg in most samples), followed by Al, Fe, Si, Sr and Zn that were at moderate levels, and then As, B, Cr, Cu, Mn, Ni, Pb, Se, Sn, Ti, V and W were at lower levels (Table 2). Overall, elements of Al, B, Ca, Cr, Fe, K, Mg, Mn, P, S, and Zn were significantly lower in the diadromous populations than those in the residential populations (Table 2). The concentrations of As and Cu (except TH-S) were significantly higher in the diadromous populations than those in the residential populations (Table 2). In general, elements of B, Cr, P, and S were lower in spawners before laying eggs than after laying eggs in the TH residential population (Table 2). The concentrations of Al and Fe were higher in spawners before laying eggs than after laying eggs in the TH residential population (Table 2). 3.2. Group patterns based on element concentrations The first two principal components (PC 1 & 2) explained 50.2% of the total variance. Differences observed in the element pattern of the six groups were primarily due to the characteristic element fingerprint of Mg, S, Ca, P, K, Zn, Mn, As, B, Cr, Cu, Si, Al, Sr, Ti, V and Se that presented for each group (Fig. 2). Diadromous DH, YJG and WH groups were associated with positive PC 2 and negative PC 1 scores whereas the residential groups were associated with both positive PC 1 and PC 2 scores except the TH-S group (Fig. 2). Similar patterns were obtained in a dendrogram through the HCA (Fig. 3). The original data matrix was divided into four main clusters, which showed a clear discrimination between the diadromous and residential groups (Fig. 3). In diadromous groups, the DH and YJG were classified into one group, and WH was classified as the other. In the residential groups, the TH-S and the other three (TH, ZY and DTH) were classified as the rest two main clusters (Fig. 3). The classification of the two populations (diadromous and residential) was 100% accurate in total based on the discriminant functions 1 and 2 plot (Fig. 4). The diadromous populations were clustered at the lower left while the residential populations were clustered at the upper right (Fig. 4). 4. Discussion The elemental contents of a fish depends on several factors, including fish species, food resources, and environmental conditions. In our study, the differences between the two populations, diadromous and residential populations, of C. nasus
Fig. 2. Principal Components Analysis (PCA) plot of six C. nanus groups in the Yangtze River Basin, China.
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Fig. 3. Hierarchical Cluster Analysis (HCA) dendrogram of six C. nanus groups in the Yangtze River Basin, China.
were striking. This may be related to the difference of environmental conditions (e.g. salinity, temperature, mineral composition, and pH) where these populations primarily experienced. For example, the Chinese mitten crabs (Eriocheir sinensis) from different lakes (Taihu Lake, Shijiu Lake and Gucheng Lake) in Jiangsu Province were consistently identified because of the environmental difference among these lakes (Yang et al., 2012). The results from PCA, HCA and LDA showed that the ZY group was classified as the residential population. In the YZ group, juvenile diadromous population of C. nasus
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Fig. 4. Linear Discriminant Analysis (LDA) plot of six C. nanus groups in the Yangtze River Basin, China.
were wild caught at the Yangtze estuary while artificially fed in ponds with fresh water. This further supported that the primary reason of the population disparity was most likely due to the difference of primary growing environmental conditions. The diadromous populations did not feed until they laid eggs and back to the sea Therefore, the differences of element contents in fish body were mainly attributed to environment variations and their physiological costs (e.g. sustaining life, migration and spawning). From a physiological point, Ca, K, Mg, and Na concentrations of fishes in the sea should be higher than those in freshwater due to their roles in regulating osmotic pressure. However, the concentrations of these elements in those of the sea groups, like DH group, actually showed lower than the others. This may be caused by the difference of the Fullness of the fish samples. The Fullness was calculated from wet weight (W) and standard length (L) at Fullness ¼ (W/L3) *100. We found that the Fullness showed a gradual decrease from DH to WH as our samples moved towards the inland, with 0.45 (0.03), 0.43 (0.02) and 0.32 (0.01), respectively. This is because they not only migrate extensively but also complete the development of gonad (Guan et al. 2010). When a fish has a lower level of Fullness, it has fewer lipids in its muscle and viscera. The concentrations of the elements will decrease when body lipid content decreases. Elemental fingerprint analysis has long been used in fish ecological studies, especially in distinguishing fish populations or stocks (Campana et al., 1994, 1995, 2000; Brazner et al., 2004). However, the traditional elemental fingerprints approach in fish ecological studies primarily used the elemental composition of fish otoliths, rather than the other tissue or even the whole body. This method is based on two aspects: (1) otoliths grow throughout the life of the fish, and unlike bones, are metabolically inert, therefore, newly deposited material is neither resorbed nor reworked after deposition (Campana and Neilson, 1985), and (2) the calcium carbonate and trace element that make up 90% of otolith appear to be mainly derived from the water (Simkiss, 1974). Accordingly, the elemental chemicals uptake onto the growing otolith may reflect the physical and chemical environment the fish live (Fowler et al., 1995; Gallahar and Kingsford, 1996), albeit with significant physiological regulation (Kalish, 1989; Farrell and Campana, 1996). So this approach has drawn many interests in recent decades (Campana et al., 2000). However, this traditional approach of elemental fingerprints has some obvious shortcomings in application: (1) otolith of fish is not always easily available, or big enough to be prepared for further measurement upon available, especially for those of small sized or at young age or earlier life stage; (2) measuring the chemicals in otoliths is not an easy task, it needs complicated pre-treatment such as the laser ablation, which might limit its application; (3) since otolith is metabolically inert and small in size, it may not be so sensitive as to be able to reflect any change of habitats that is as less significant as the shifts between freshwater and marine environments, and (4) since fish otoliths do not involve the metabolism of the fish, this approach can only be used to trace the environment change of fish, it can not reflect the ecological interactions between fish and its environment. The information that the Sr: Ca ratio of the fish otolith can convey would be very limited, though it is a good indicator for the shift between freshwater and marine environments.
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Compared to this traditional approach, the current approach we used here analyzed the whole body chemical composition, which is usually more informative than those of otoliths of the fish. Because the chemicals of fish body are actively involving the metabolism, their composition is resulted from the interactions between the fish and environment. That is, EFA can tightly link to ecological stoichiometry, and thus can reveal more information than otolith elemental fingerprints in understanding of the interactions between fish and habitat. Our current results demonstrated that using whole body EFA could not only clearly distinguish the migratory population of Coilia nasus from the residential, but also the spawners from the non-spawners. EFA of either part or the whole body of an organism has traditionally or most often been used in identifying the localities of certain agricultural products such as teas, barley and wines, etc. (Marcos et al., 1998; Hill, 1998; Husted et al., 2004; Gonzalve et al., 2009). It has even been successfully used to identify the differentiation between cultivars of tea planted in the same plantation (Chen et al., 2009). Our current results indicated that this approach could also be used in animal ecological study such as fish ecology with satisfactory results. This might be because: (1) the food sources of the fish are often more locally distributed, and could better reflect the tributes of the environment. So even if the fish might be a migratory species or a newly inhabitator to a certain habitat, when it moves to a new environment, it will be imprinted with this new environment through the feeding on the local food; (2) the chemical composition in fish food and water can also affect the fish body chemical compositions through either osmoregulation or active absorption of the fish. Since the chemicals of the fish body are all actively involved in the metabolism, they are more easily changed by new environment than those of the fish otoliths, thus are more sensitive indicators of habitat shifts. For example, from our current results, we could even distinguish samples of the migratory C. nasus between the YJG and WH groups in the Yangtze River. It will be difficult to distinguish the two groups if fish otoliths were used, because it will not take a long time (usually within months) for the fish to migrate from the Yongji Harbor (YJG) to Wuhu City (WH) for spawning where the chemical composition of the otoliths may not change substantially. As a summary, C. nasus is a taxonomically controversial species in China, with many different eco-types of populations, and it is usually difficult to distinguish them from each other either morphologically or molecularly. Our results showed that these populations, even for the different migratory stages of the diadromous populations, can be distinguished using the whole body chemical composition. The whole body EFA may be applicable in other fish ecological studies as well. Acknowledgments We thank all the personals who have provided a lot of assistance during the field sampling and lab analysis. 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