Species composition and assemblages of ichthyoplankton during summer in the East China Sea

Continental Shelf Research 126 (2016) 64–78

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Research papers

Species composition and assemblages of ichthyoplankton during summer in the East China Sea Han-Yang Lin a,b, Mei-Yun Chiu a,b, Yu-Ming Shih a, I-Shiung Chen b, Ming-An Lee c, Kwang-Tsao Shao a,b,n a

Biodiversity Research Center, Academia Sinica, Taipei 11529, Taiwan Institute of Marine Biology, National Taiwan Ocean University, Keelung 20224, Taiwan c Department of Environmental Biology and Fisheries Science, National Taiwan Ocean University, Keelung 20224, Taiwan b

art ic l e i nf o

a b s t r a c t

Article history: Received 15 January 2016 Received in revised form 14 July 2016 Accepted 26 July 2016 Available online 27 July 2016

The East China Sea (ECS) is one of the most important fish spawning and nursery grounds in the north Pacific. Even though summer is an important spawning season for many fishes in the region, large-scale molecular identification studies on ichthyoplankton during this season are few. In this study, we sampled 8,933 fish eggs and 12,161 fish larvae from 25 stations during the summer of 2009. Using DNA barcoding, a number of the fish eggs and larvae were identified and classified into 45 and 124 taxa, respectively. Principal component analysis (PCA) categorized the inshore stations of the Changjiang Diluted Water area as having the hydrographic features of low sea surface temperature (SST), salinity (SSS) and high chlorophyll a (SSC) contents, whereas the continental shelf and offshore stations under the influence of the Kuroshio Current displayed the opposite results. Ichthyoplankton was more abundant at the inshore stations than the offshore stations, but species diversity was lower at the former locations. Species compositions of both fish eggs and fish larvae at the 25 stations were categorized into three different assemblages based on a non-metric multidimensional scaling analysis. Combining the assemblage patterns of ichthyoplankton with the results of the PCA and satellite images of SST and SSC showed that the assemblage patterns of fish eggs were correlated with water mass, while those of the fish larvae were not. & 2016 Elsevier Ltd. All rights reserved.

Keywords: Fish eggs Fish larvae Species composition and assemblage DNA barcoding East China Sea

1. Introduction The East China Sea (ECS) in the northwestern Pacific is a crucial continental shelf ecosystem with a total area of approximately 7.7
105 km2. Environmental variables such as water depth, sea surface temperature (SST), sea surface salinity (SSS), and sea surface chlorophyll a (SSC) vary in a gradient from inshore to offshore areas (Gong et al., 1996, 2003, 2006; Chen et al., 2007). Because of the influence of several currents and water masses, notably the Kuroshio Current, Kuroshio Branch Current (KBC), Changjiang Diluted Water (CDW) and the Taiwan Warm Current (Chen et al., 1994; Katoh et al., 2000; Ichikawa and Beardsley, 2002) (Fig. 1), the hydrographic conditions of the ECS vary in different seasons. In winter, the CDW follows a narrow coastal jet (China Coastal Current) southward (Lee and Chao, 2003; Liu et al., 2010) and the Kuroshio intrusion onto the ECS shelf is also diminished. In n Corresponding author at: Biodiversity Research Center, Academia Sinica, Taipei 11529, Taiwan. E-mail address: [email protected] (K.-T. Shao).

http://dx.doi.org/10.1016/j.csr.2016.07.016 0278-4343/& 2016 Elsevier Ltd. All rights reserved.

summer, the CDW brings abundant freshwater into the ECS and it may spread out several hundred kilometers from the river mouth (Beardsley et al., 1985; Gong et al., 1996; Lee and Chao, 2003). The Kuroshio Current also strengthens to intrude onto the ECS shelf. These conditions provide nutrients into the ECS and consequently high primary productivity during the summer (Liu et al., 2000; Zhang, 2002; Liu et al., 2010), making it a vital fish spawning and nursery ground. FishBase currently lists 1,099 fish species in the ECS (Froese and Pauly, 2016). Nevertheless, fishery resources in the region are rapidly declining due to overfishing (Coll et al., 2008). As a result, information on fish spawning and recruitment is particularly essential. In the past, reproductive information on marine fish was usually assessed by observing gonad development (Hickling and Rutenberg, 1936; Chellappa et al., 2010). However, the habitats of adult marine fish vary from neritic areas, such as coral reefs and rocky, muddy, and sandy shores, to oceanic areas, such as the epipelagic, mesopelagic, and bathypelagic zones. Collecting fish from different habitats requires different sampling tools, which can be expensive and time intensive. In contrast, directly investigating the spatial and temporal distributions of fish eggs and larvae is an ideal

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Fig. 1. The East China Sea is influenced by different currents (modified after Ichikawa and Beardsley (2002)). CDW: Changjiang Diluted Water; TWC: Taiwan Warm Current; KBC: Kuroshio Branch Current. In total, 25 stations were established in this survey and divided into transects A–F; inshore stations (depth r 60 m), continental shelf stations (depth 60–100 m), and offshore stations (depth4100 m); SECS (transects A and B), CECS (Station 7, transect C, and Station 16), and NECS (transects D–F).

alternative. Since diverse fish eggs and larvae can be captured through zooplankton net sampling, researchers can more easily and precisely identify the spawning and nursery grounds and the spawning period through ichtyoplankton assessments (Sassa and Tsukamoto, 2010; Ellis et al., 2012; Hsieh et al., 2012). Most ichthyoplankton studies conducted in the ECS focused on commercial fish species, such as Japanese Anchovy (Iseki and Kiyomoto, 1997; Kim et al., 2005; Takasuka and Aoki, 2006), Hairtail fish (Kim et al., 2005; Lee and Kim, 2014) and Mackerel (Kasai et al., 2008; Sassa et al., 2016), or in particular areas, such as the Changjiang estuary (Iseki and Kiyomoto, 1997; Wan et al., 2010), the southern ECS (Wang et al., 2013; Sassa and Konishi, 2015), and the shelf-break region (Okazaki and Nakata, 2007). Moreover, most of them were carried out in either spring or autumn. Summer is also an important spawning period for many fishes, but the region lacks large-scale multi-species investigations during this season. Additionally, the ichthyoplankton in most studies have been identified by morphological characteristics. Previous studies have shown that DNA barcoding can more accurately identify the early life stages of fish than traditional morphological methods (Valdez-Moreno et al., 2010; Ko et al., 2013; Harada et al., 2015; Hubert et al., 2015). Therefore, we chose to use DNA barcoding to identify the ichthyoplankton collected from broad-scale sampling in the ECS in summer. The study is part of the Long-term Observation and Research of the East China Sea (LORECS) project; a multidisciplinary project that began in 1997 to understand the biogeochemical features of the ECS (Gong et al., 2007) and that includes fish studies. However, before 2009, the studies mainly focused on adult fishes (Chang et al., 2012) and fish larvae (Chen et al., 2014), but not fish eggs. In order to acquire more fundamental biological information about fish in the ECS, the LORECS project added this study in 2009. By analyzing the relationship between ichthyoplankton distribution patterns and environmental variables, we can further identify which environmental conditions fishes prefer to live in (Hernández-Miranda et al., 2003; Azeiteiro et al., 2006; Roussel et al.,

2010). In this study, we examine the compositions and assemblages of fish eggs and larvae through DNA barcoding and assess the influence of environmental variables on the distribution of ichthyoplankton in the ECS.

2. Materials and methods 2.1. Data collection The sampling area was defined as 120°5′–127°0′E and 25°5′– 32°29′N, which included 25 stations divided amongst six transects (A–F) in three subareas: Southern ECS (SECS; transects A and B), Central ECS (CECS; station 7, transect C, and station 16), and Northern ECS (NECS; transects D, E, and F). According to their distances from the shoreline and water depths, these 25 stations were grouped as follows for further analysis: inshore stations (stations 4, 5, 30, 29, 28, 20, and 41; listed in ascending order of latitude) with depths up to 60 m, continental shelf stations (stations 3, 35, 7, 31, 32, 16, 27, 22, 37, and 39) with depths between 61 and 100 m, and offshore stations (stations K, 1, 2, 33, 34, 12, 26, and 24) with depths of more than 100 m (Fig. 1). The fish eggs and larvae were collected aboard R/V Ocean Research I between June 29 and July 13, 2009, using a round-mouth ichthyoplankton (RMI) net with a mouth diameter of 1.3 m and mesh size of 1.0 mm. The net was towed obliquely at a depth of 200 m to the surface at deep stations (depths 4200 m) and at 10 m from the sea bottom to the surface at shallow stations (depths o200 m). A flow meter was attached to the center of the net to calculate the volume of filtered seawater. The zooplankton samples were fixed in 95% ethanol for further molecular experiments. 2.2. Classification and sampling for DNA barcoding Fish eggs and larvae were sorted at each station and classified

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into different types according to the morphological characteristics reported by Mito (1961), Okiyama (1988), Leis et al. (1989), Neira et al. (1998), and Kendall (2011). For each morphological type of fish eggs and larvae with well-developed embryos, one individual was selected. However, numerous unfertilized or early-developed fish eggs lacking clear morphological characteristics and shared morphologies with different species could not be identified using a morphological approach. Thus, identifying all unfertilized or earlydeveloped eggs through DNA barcoding appeared to be a more accurate approach to prevent misidentification. However, DNA barcoding all samples would not have been feasible. Therefore, we subsampled the collection to balance cost and representativeness. The subsampling rules were as follows: 1, 3, 5, 10, and 15 individuals were picked for the morphological types with r50, 51– 100, 101–200, 201–300, and 4 301 individuals, respectively. Finally, based on the ratio, the numbers of each taxon in each type were determined. Fish eggs and larvae were identified to the lowest possible taxonomic level using DNA barcoding. 2.3. DNA barcoding Whole eggs and a section of the tail muscle of fish larvae were used for DNA extraction. DNA extraction and PCR amplification were performed as described by Ko et al. (2013) and Chang et al. (2016). The Barcode of Life Database (BOLD) (http://www.boldsys tems.org/) and its BLAST tools was the principle database used in this study for sequence comparison and species identification. We recognized specimens to species and genus levels if the similarity values were greater than 99% and 97–98.99%, respectively (Ratnasingham and Hebert, 2007). Another sequence database, Cryobank Program For Wildlife Genetic Material in Taiwan (CRYOBANK), which includes 3,729 records of 35 orders, 205 families, 700 genera, and 1,419 species — representing approximately half of the entire Taiwanese fish species assemblage — was used for further comparison. In CRYOBANK, sequences were aligned using the BioEdit software version 7.0.8.0 (Hall, 1999). Subsequently, we used the software MEGA 4 (Kumar et al., 2008) to generate the neighbor-joining (NJ) trees of the Kimura two-parameter (K2P) distance to graphically represent the patterns of divergence between species (Saitou and Nei, 1987). To substantiate the reliability of the NJ trees, 1000 bootstrap replications were performed. The K2P genetic distances for defining the species, genus, and family levels were based on Ward et al. (2005). 2.4. Data analysis Along with the ichthyoplankton, data on four environmental variables — depth, SST, SSS, and SSC —were collected simultaneously from the 25 stations. After normalizing the data, we used principal component analysis (PCA) to extrapolate the water mass distribution patterns throughout the 25 stations. The number of ichthyoplankton was standardized as abundance (individuals 10  3 m  3) and transformed using the logarithmic function [log (abundance þ1)]. Non-metric multidimensional scaling (NMDS) using Bray–Curtis similarity was performed to clarify the distribution patterns of the ichthyoplankton assemblages. Moreover, the PCA results and satellite images of SST and SSC were combined with the NMDS results to chart the relationship between the distribution patterns of ichthyoplankton assemblages and water masses. The major taxa contributing to each assemblage and their contribution rates (above 2%) were analyzed on the basis of similarity percentages (SIMPER). All aforementioned analyses were performed using the Plymouth Routines in Multivariate Ecological Research statistical software package version 6.0 (Clarke, 1993; Clarke and Gorley, 2006). Furthermore, the distribution patterns of all ichthyoplankton taxa in the 25 stations combined with data on

the four environmental variables were included in canonical correlation analysis (CCA) using the Multivariate Statistical Package version 3.1 (Kovach, 2007) to analyze the types of environments these ichthyoplankton preferred to live in.

3. Results 3.1. Hydrographic conditions Inshore stations with a depth below 60 m exhibited similar SST (Fig. 2a) and SSS distributions (Fig. 2b), and low temperature and salinity (mean SST: 25.3 71.1 °C, mean SSS: 31.8 71.6). With increasing distance from the inshore stations, SST and SSS increased gradually from continental shelf stations (mean SST: 27.271.3 °C, mean SSS: 32.9 71.3) to offshore stations (mean SST: 27.471.1 °C, mean SSS: 33.670.5). In contrast, the SECS, directly influenced by the Kuroshio Current, presented the highest SST (mean SST: 27.671.1 °C) and SSS (mean SSS: 33.6 70.5), both of which decreased with increasing latitude (CECS, mean SST: 27.371.8 °C and mean SSS: 33.770.2; NECS, mean SST: 25.6 71.1 °C and mean SSS: 31.571.4). The ranges of SST and SSS were as high as 5.82 °C and 5.22, respectively. Overall, SSC (Fig. 2c) exhibited a dissimilar distribution. The highest SSC content was observed at the inshore stations (mean: 2.4 71.4 mg m  3) and the NECS area (mean: 1.2 71.2 mg m  3), and it declined towards the continental shelf (mean: 0.4 7 0.3 mg m  3) and offshore stations (mean: 0.3 70.3 mg m  3). The SSC gradient was steeper than that of the SST and SSS gradients. The PCA results indicate that, except for station K and station 41, stations could be categorized into two groups: a CDW-influenced area (low SST and SSS, but high SSC) and a Kuroshio-influenced area (high SST and SSS, but low SSC) (Fig. 2d). Station K and station 41 were outliers from these groups because of their greater water depth and SSC, respectively. 3.2. Abundance of ichthyoplankton Among the 25 stations, the depths at station K (1,687 m) and station 1 (267 m) exceeded 200 m, and we sampled at a depth of 200 m below the surface. The depths of the remaining 23 stations (34–117 m) were less than 200 m, and we sampled at 10 m from the sea bottom. In total, 8,933 fish eggs and 12,161 fish larvae were collected from these 25 stations, with a total abundance of 21,165 ind. 10  3 m  3 and 26,110 ind. 10  3 m  3, respectively. Both fish eggs and fish larvae were collected from all stations, except for station 41, where only fish eggs were collected. Fish eggs and larvae were most abundant in the inshore areas, especially in the estuary. For example, station 4 near the estuary of the Minjiang River had the highest abundance of fish eggs (5,224 ind. 10  3 m  3), and station 29 close to the estuary of the Qiantang and Changjiang Rivers had the highest abundance of fish larvae ( 7,473 ind. 10  3 m  3) (Table 1). Similar to the SSC distribution, the abundances of fish eggs and larvae decreased gradually with increasing distance from the shoreline (Table 2). Fish eggs were most abundant in the SECS and fish larvae were most abundant in the NECS, possibly because few species lay eggs in large numbers and because some fish larvae prefer to aggregate in particular areas (Table 2). In contrast, the diversity of fish eggs and larvae exhibited the opposite trend; the offshore and SECS stations presented the highest diversities, with diversity decreasing towards the inshore and NECS stations (Table 2). In addition, regression analysis between the three biological variables (abundance of ichthyoplankton, taxa number, and diversity index) and the four environmental variables (depth, SST,

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Fig. 2. Spatial distribution of three environmental variables and results of Principal Component Analysis (PCA). (a) Sea surface temperature (°C), (b) sea surface salinity, (c) sea surface chlorophyll a (mg L  1), and (d) PCA results.

SSS, and SSC) at the 25 stations indicated that the abundance of fish larvae was significantly and positively correlated with SSC (p o0.05), whereas that was not the case for eggs. The numbers of taxa of fish eggs and larvae were both significantly and positively correlated with SST and SSS, whereas only the number of taxa of fish eggs was significantly and positively correlated with SSC (the concomitant value for fish larvae did not reach statistical significance, p ¼0.16). Similarly, the diversity indices of fish eggs and larvae were both significantly and positively correlated with SST and SSS (p o0.01). The diversity index of fish larvae (p ¼0.05), but not that of fish eggs (p ¼0.06), was significantly and negatively correlated with SSC (Table 3). 3.3. DNA barcoding to identify ichthyoplankton composition After morphological classification, 8,933 fish eggs and 12,161 fish larvae were categorized into 88 and 175 morphological types, respectively. Overall, 517 samples, including 342 fish eggs and 175 fish larvae, were used for molecular experiments. DNA barcoding revealed 45 taxa of fish eggs, of which 6 were identified to family level, 6 to genus level, and 33 to species level. Additionally, 124 taxa of fish larvae were revealed, of which 38, 18, and 68 were identified to family, genus, and species levels, respectively. The success rates of identification were 95.61% for fish eggs and 98.86%

for larvae. Identification failures (15 fish eggs and 2 fish larvae) were due to PCR failure or the absence of matching sequences in BOLD or CRYOBANK. In total, 150 different taxa of fish eggs and larvae were identified in the ECS, of which 44, 23, and 83 were identified to the family, genus, and species levels, respectively. These 150 taxa accounted for 13.65% of the 1,099 species listed in FishBase (Table 4). Eighteen taxa were common to both fish eggs and larvae, accounting for 59.13% of the eggs and 35.95% of the larvae in the study (Table 4). Many dominant taxa (abundance 42%) of fish eggs and fish larvae were commercially valuable fishes. Bullet tuna (Auxis rochei), Tonguefishes (Cynoglossus arel and C. interruptus) and Hairtail fish (Trichiurus japonicus) accounted for 54% of total eggs (Fig. 3a). Anchovies (Engraulis japonicus and Engraulidae), Croakers (Atrobucca nibe, Pennahia argentata, and Johnius sp.) and Bullet tuna accounted for 45% of total larvae (Fig. 3b). 3.4. Assemblage patterns of fish eggs and larvae and their relationship to environmental variables The PCA results showed that the 25 stations could be classified as CDW- and Kuroshio-influenced areas. We combined the results of the PCA with data on the abundance of fish eggs and larvae to generate the NMDS results. The NMDS revealed that the

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Table 1 Abundance (ind. 10  3 m  3), taxa number, and Shannon diversity index of fish eggs and larvae in the East China Sea. Area

Transect

Station

Taxa Number

H′

Abundance

Taxa Number

H′

A

K 1 2 3 4

45 208 291 814 5224

13 8 6 11 9

2.403 2.021 1.598 2.23 2.128

131 161 541 387 3862

26 26 26 25 17

3.043 3.014 3.136 3.124 2.806

B

33 34 35 5

704 609 4061 646

6 8 12 6

1.766 2.032 2.307 1.747

692 587 658 2886

40 49 48 37

3.615 3.743 3.779 3.498

Station 7

7

208

7

1.862

189

21

2.925

12 32 31 30 16

140 522 1387 459 175

11 11 8 3 10

2.247 2.264 2.039 1.088 2.178

412 82 2546 673 213

23 21 40 12 26

2.992 2.963 3.563 2.405 3.144

D

26 27 28 29

356 999 470 1020

6 10 10 10

1.759 2.163 2.272 2.135

147 704 3099 7473

18 25 40 42

2.779 3.116 3.604 3.57

E

24 22 20

132 17 1940

8 1 4

2.03 0 1.379

118 202 291

20 14 9

2.887 2.535 2.113

F

37 39 41

7 693 39

1 3 1

0 1.074 0

7 47 0

2 7 0

0.6763 1.824 0

C Station 16

NECS

Larvae

Abundance

SECS

CECS

Eggs

H′: Shannon–Weaver diversity index. Table 2 Average abundance (ind. 10  3 m  3) and Shannon diversity index of fish eggs and larvae from different areas in the East China Sea. Stations

Mean abundance H' (eggs) (eggs)

Inshore stations Continental shelf stations Offshore stations NECS CECS SECS

Mean abundance H' (larvae) (larvae)

13117 1568 9107 1201

1.487 0.71 1.677 0.90

22917 2430 554 7 740

2.83 7 0.69 2.87 7 0.85

3497 210 567 7586 4827 430 14007 1771

1.92 7 0.21 1.28 70.91 1.95 7 0.41 2.03 70.26

380 7 220 1209 7 2270 686 7854 1101 71251

3.167 0.34 2.317 1.13 3.007 0.34 3.31 70.34

Table 3 Results of regression analysis between biological and environmental indices (*mean p o 0.05, **mean p o 0.01). Items

Depth

SST

SSS

SSC

Abundance (eggs) Abundance (larvae) Taxa (eggs) Taxa (larvae) H′ (eggs) H′ (larvae)

 0.168  0.147 0.361 0.054 0.234 0.085

 0.01  0.221 0.659** 0.528** 0.600** 0.589**

0.044  0.023 0.686** 0.607** 0.775** 0.785**

0.279 0.393*  0.392*  0.289  0.38*  0.396*

Depth (m); SST: sea surface temperature (°C), SSS: sea surface salinity; SSC: sea surface chlorophyll a (μgL  1); abundance (ind. 10  3m  3); H′: Shannon–Weaver diversity index.

abundances of fish eggs and larvae at the 25 stations could be classified into three respective assemblages: (1) for fish eggs, the continental shelf assemblage, the inshore assemblage, and the CDW assemblage; and (2) for fish larvae, a mixed-shore assemblage, a northern-offshore assemblage, and a CDW assemblage

(Fig. 4). In addition, we plotted the results of assemblages of fish eggs and larvae onto satellite images of SST and SSC to determine the relationship between the distribution patterns of assemblages and water masses (Fig. 5). The distribution patterns of the assemblages of the fish eggs fit well with the water masses. The stations belonging to the Kuroshio-influenced area were perfectly grouped into the continental shelf assemblage, whereas those belonging to the CDW-influenced area were grouped into two further assemblages: (1) inshore stations were classified into an inshore assemblage; and (2) the three stations of transect F were grouped into a CDW assemblage (Figs. 4a, 5a, and b). In contrast, the distribution patterns of fish larvae assemblages did not fit well with the water masses. Two stations (stations 37 and 39) of transect F in the CDW-influenced area were classified into the CDW assemblage. Six stations in the Kuroshio-influenced area were located in the central–northern ECS, which were classified into a northern-offshore assemblage. The remaining 16 stations (comprising station K and 6 and 9 stations belonging to the CDW- and Kuroshio-influenced areas, respectively) were categorized into a mixed-shore assemblage (Figs. 4b, 5c, and d). 3.5. Species composition of each assemblage The SIMPER analysis revealed that the inshore assemblage of fish eggs contained 19 taxa; five major taxa, including Coryphaena hippurus and C. interruptus, contributed 93.64% to this assemblage. The CDW assemblage of fish eggs included three taxa, with Erisphex pottii being the major contributing taxon. Among the three assemblages of fish eggs, the continental shelf assemblage had the most taxa (35 taxa), including eight major taxa; A. rochei, Diodon holocanthus, and T. japonicus contributed 92.2% to this assemblage (Table 5). The compositions of two stations (Station K and 22) were outliers to the three main fish egg assemblages (Fig. 4a).

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Table 4 All 150 taxa of fish eggs and larvae in the East China Sea. Taxa Abudefduf vaigiensis Acanthurus nigrofuscus Acropoma japonicum Ammodytes sp. Ammodytidae Antennarius striatus Apogon sp. Apogonidae Asterropteryx semipunctata Atrobucca nibe Aulotrachichthys prosthemius Auxis rochei Auxis thazard Balistapus undulatus Bathycallionymus kaianus Blenniidae Bothidae Brama dussumieri Brama sp. Branchiostegus japonicus Bregmaceros sp. Bregmacerotidae Carangidae Carapidae Cephalopholis boenak Champsodon snyderi Chauliodus sloani Chauliodus sp. Cheilopogon sp. Chrysochir aureus Clupeidae Congridae Coris picta Coryphaena hippurus Myersina filifer Cubiceps sp. Cyclothone pallida Cynoglossidae Cynoglossus arel Cynoglossus interruptus Decapterus kurroides Decapterus macarellus Decapterus sp. Dendrochirus bellus Diagramma picta Diaphus richardsoni Diodon holocanthus Encrasicholina punctifer Engraulidae Engraulis japonicus Engyprosopon sp.

Eggs %

0.010

28.387

0.113

4.738

Larvae%

Taxa

Eggs %

0.018 0.027 0.007 0.020 0.064 0.004 0.113 0.308 0.032 4.437 0.002 2.361 1.153 0.009 0.199 0.011 0.116 1.043 0.209 0.009 0.006 1.519 0.387 0.011 0.003 0.438 0.048

Erisphex pottii Erythrocles scintillans Euthynnus affinis Fistulariidae Gadidae Gerreidae Gobiidae Gobiesocidae Gonostomaatidae Halichoeres melanochir Harpadon nehereus Heteromycteris japonicus Heteromycteris sp. Hoplolatilus marcosi Howella sp. Jaydia lineatus Johnius sp. Labridae Lagocephalus inermis Lampanyctus alatus Lampanyctus sp. Lampris guttatus Leiognathidae Lepidotrigla guentheri Lutjanus rufolineatus Lutjanus russellii Mene maculata Minous monodactylus Minous sp. Molidae Moridae Muraenesox cinereus Myctophidae Nemipterus virgatus Neobythites sivicola Nettastomatidae Odontanthias borbonius Onigocia spinosa Ophichthidae Ophichthidae gen. sp.1 Ophichthidae gen. sp.2 Ophichthidae gen. sp.3 Ophichthidae gen. sp.4 Otolithes ruber Paralepididae Paraplagusia japonica Pennahia anea Pennahia argentata Pennahia sp. Plagiopsetta glossa Platycephalidae

1.863 0.030 0.105

0.004 0.102 0.253 0.046 0.093 0.162 2.718

0.302 0.622

0.046 0.004 2.164 15.895 6.718 1.580

4.571

2.384

0.738 0.394 1.606 0.017 0.180 0.008 0.003 0.283 1.506 4.570 26.029 0.055

Station K had the greatest water depth, and most of the fish eggs captured there belonged to deep-sea fish species, such as Cubiceps sp., Chauliodus sp., and Ophichthidae gen. sp.3. Only one species, T. japonicus, was captured at Station 22. There were 108 taxa of fish larvae in the mixed-shore assemblage, including 15 major taxa, with E. japonicus, Gobiidae, and Engraulidae contributing 74.04% to this assemblage. The northern-offshore assemblage of fish larvae comprised 57 taxa, including 15 major taxa, with A. rochei, Ophichthidae, and Jaydia lineatus contributing 90.52% to this assemblage. The major taxa of fish larvae contributing to the CDW assemblage were Harpadon nehereus and Gobiidae (Table 5). 3.6. Relationship between the distribution of ichthyoplankton and water masses In the previous section, we reported that the assemblage patterns of fish eggs fit well with the patterns of water masses and

3.792 0.421 0.057

Larvae%

0.046 0.044 2.177 0.037 10.131 0.011 0.030 0.027 1.219 0.284 0.003 0.003 1.274 2.419 0.042 0.204 0.003 0.006

0.007 0.009 0.036 0.002 0.017 0.018 3.718 0.053

0.057 0.081 0.344

0.322 0.195

8.128 0.081 0.021 0.364 0.007 0.042 2.413

0.568 0.021 0.116 0.045 0.075 0.019 1.120 1.346 3.829 0.011 0.019

Taxa Pleuronectidae Polyductylus sextarius Pomacentridae Priacanthus macracanthus Psenopsis anomala Repomucenus sp. Rhabdamia gracilis Rogadius sp. Samaridae Sarda orientalis Sardinella melanura Saurida elongata Scarus ghobban Sciaenidae Scomber australasicus Scomberoides sp. Scombridae Scopelarchidae Scorpaenidae Scorpaenopsis venosa Secutor ruconius Seriola dumerili Serranidae Setipinna tenuifilis Sillago japonica Sillago sp. Soleidae Spratelloides gracilis Stephanolepis cirrhifer Stomiidae Strophidon sathete Syngnathidae Synodontidae Synodus macrops Trachinocephalus myops Synodus sp. Terapon jarbua Tetraodontidae Thryssa sp. Thunnus tonggol Trichiuridae Trichiurus japonicus Upeneus japonicus Uranoscopus oligolepis Valamugil sp. Valenciennea wardii Vinciguerria sp. Xiphias gladius Unknown Total

Eggs %

0.056

Larvae% 0.025 0.113 0.013 0.223 0.129 0.648 0.354 0.201

0.029 0.177 0.073 0.333 0.028 0.962 0.060 0.008 0.297 0.222 0.303 0.014 0.311 0.770 0.040 2.974

0.292 0.020 0.061 0.149 0.216 0.008 0.006

0.577 0.007 0.173 1.224 0.634

2.407 2.835 0.060 2.667

0.717 0.118 0.003 0.134

0.572 1.283 0.021

0.114

5.403 100

1.497 2.809 0.024 0.019 1.408 100

0.010

that those of the fish larvae did not. We analyzed all 45 taxa of fish eggs and 124 taxa of fish larvae, together with the 4 environmental variables, in a CCA to determine water mass preferences. The CCA results indicated that 34 taxa of fish eggs and 93 taxa of fish larvae could be classified into a Kuroshio-influenced group because of their positive relationship with SST and SSS. The remaining 11 taxa of fish eggs and 31 taxa of fish larvae could be categorized into a CDW-influenced group because their distributions were positively related to SSC (Fig. 6). A comparison of the distribution patterns of the 18 common taxa of fish eggs and larvae revealed that nine taxa—Acropoma japonicum, A. rochei, Decapterus kurroides, D. holocanthus, Euthynnus affinis, Heteromycteris japonicus, Minous sp., Nemipterus virgatus, and Trachinocephalus myops—belonged to the Kuroshioinfluenced group and two taxa—C. interruptus and E. japonicus— belonged to the CDW-influenced group. Of the remaining seven taxa, five—Brama dussumieri, Champsodon synderi, H. nehereus,

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Fig. 3. Dominant taxa of (a) fish eggs and (b) fish larvae in the East China Sea.

Psenopsis anomala, and T. japonicus—belonged to the Kuroshioinfluenced group of fish eggs and the CDW-influenced group of fish larvae, whereas the other two taxa—C. hippurus and Setipinna tenuifilis—exhibited the opposite pattern (Table 6).

4. Discussion 4.1. DNA barcoding presented more taxonomic information than a morphological approach In this study, DNA barcoding was used to identify fish eggs and fish larvae collected from the ECS in the summer of 2009. A total of 8,933 eggs and 12,191 fish larvae were identified into 45 and 124 taxa, respectively. Among them, 39 taxa of fish eggs and 86 taxa of fish larvae were identified to either the genus or species level. In the past, unfertilized or early-developed fish eggs with special

characteristics on their membranes, such as the reticular structure of Lizardfishes (Mito, 1961; Shao et al., 2001) and the three-feather root structures of Lanternfishes (Shao et al., 2001), allowed their identification to family level. Fish eggs without clear morphological characteristics or with shared morphologies have proven more difficult to classify (Shao et al., 2002; Gleason and Burton, 2012; Harada et al., 2015). However, using DNA barcoding, the eggs from our collection that were morphologically indistinguishable (accounting for 52% of the sample) were easily classified into 30 taxa. Among them, 6 were identified to the family level, 3 to the genus level, and 21 to the species level. As for fish larvae, it is not always possible to identify fish larvae to species level based on morphological characteristics (Chen et al., 2014; Sassa and Konishi, 2015). Many species of fish larvae share similar morphologies. To complicate matters, different developmental stages of the same species can have different characteristics. For example, 7 morphological types were identified as

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Fig. 4. Results of (a) fish eggs and (b) fish larvae assemblages among the 25 stations in the East China Sea through NMDS analysis.

1 taxon—Sciaenidae spp.—by the morphological approach, but subsequent DNA barcoding revealed them to be from 6 taxa— Atrobucca nibe, Chrysochir aureus, Johnius sp., Pennahia anea, Pennahia argentata and Sciaenidae. However, there are limits to DNA barcoding for species identification. In the study, 3.81% of fish eggs and 1.14% of fish larvae could not be identified because of PCR failure. The reasons for this may be because PCR amplification efficiency was diminished or eliminated by poor quality tissue samples or because inappropriate primers or PCR conditions were used. Additionally 0.58% of fish eggs were not identified because of the absence of matching sequences in BOLD or CRYOBANK, but this problem could be solved once these sequence repositories are complete. Despite the aforementioned problems, DNA barcoding still demonstrated its effectiveness for identification of ichthyoplankton, as it identified most fish eggs and larvae from this study to the genus or species level. Our results will help to add more information on the spawning period, spawning grounds and nurseries of fishes in the ECS.

4.2. The spatial distribution of fish eggs and larvae During summer, fluctuations in the SST and SSS were coordinated, with a declining gradient from the offshore to the inshore areas and from the SECS to the NECS, whereas the SSC exhibited the opposite gradient. These results are consistent with those of past studies (Gong et al., 1996, 2003, 2006; Chen et al., 2007). The 25 stations in this study were classified into Kuroshioor CDW-influenced areas based on ECS hydrography (Fig. 2d). Subsequently, the distribution of ichthyoplankton was determined using these water masses. The inshore stations, located in the CDW-influenced area, were characterized by low temperature and salinity and high chlorophyll a content (Figs. 2 and 5). Although the CDW is the main influence in this area, other rivers such as the Qiantang, Oujiang, and Minjiang also carry nutrients here (Gong et al., 1996, 2003, 2006; Chen et al., 2007). The abundance of plankton increases in sea areas rich in nutrients (Sabatini et al., 2004), consequently increasing the abundance of fish larvae because plankton is their major food source (Hsieh et al., 2010; Chen

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(b) Fish eggs-SSC

Latitude (°N)

(a) Fish eggs-SST

Kuroshio assemblage

Kuroshio assemblage

Longitude (°E)

Longitude (°E)

(d) Fish larvae-SSC

Latitude (°N)

(c) Fish larvae-SST

Northernoffshore assemblage

Longitude (°E)

Northernoffshore assemblage

Longitude (°E)

Fig. 5. Fish eggs and fish larvae assemblages combined with satellite images of SST and SSC in the East China Sea. (a) Fish egg assemblage with the SST image, (b) Fish egg assemblage with the SSC image, (c) Fish larvae assemblage with the SST image, and (d) Fish larvae assemblage with the SSC image.

et al., 2012). This result explains why large numbers of fish larvae were present at the inshore stations, a phenomenon also reported in other studies (Gaughan et al., 1990; Chen et al., 2014). Thus, our results demonstrate that the inshore area of the ECS is a crucial spawning and nursery ground.

The results of a regression analysis between biological and environmental variables (Table 3) indicated that ichthyoplankton was abundant in the CDW-influenced area (inshore area), but diversity was low. Only 11 taxa of fish eggs and 31 taxa of fish larvae preferred this area (Table 5). In contrast, a low abundance of

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Table 5 Results of SIMPER between fish egg and larvae assemblages ((a)–(g) represent the habitats of fishes). Fish egg assemblages Inshore assemblage Station : 4, 5, 20, 29, 30 Taxon e

Coryphaena hippurus Cynoglossus interruptusd Erisphex pottiid Harpadon nehereusc Minous monodactylusd

Contrib% 49.96 21.29 9.35 6.53 6.51

Total taxa: 19

Continental shelf assemblage Station : 1, 2, 3, 7, 12, 16, 24, 26, 27, 28, 31, 32, 33, 34, 35 Taxon Contrib% e

Auxis rochei Diodon holocanthusg Trichiurus japonicusc Synodus macropsd Coryphaena hippuruse Champsodon snyderid Ophichthidae gen. sp.1c,d,g Trachinocephalus myopsg

26.71 18.62 15.96 7.79 7.06 6.09 5.95 4.02

Total taxa: 35

CDW assemblage Station : 37, 39, 41 Taxon Erisphex pottii

d

Contrib% 100.00

Total taxa: 3

Fish larva assemblages Mixed-shore assemblage Station : K, 1, 2, 3, 4, 5, 20, 22, 27, 28, 29, 30, 31, 33, 34, 35

Northern-offshore assemblage Station : 7, 12, 16, 24, 26, 32

CDW assemblage Station : 37, 39

Taxon

Contrib%

Taxon

Contrib%

Taxon

Contrib%

11.85 11.53 7.90 5.94 4.80 4.72 4.31 3.77 3.53 3.05 2.89 2.78 2.73 2.17 2.07

Auxis rocheie Ophichthidaec,d,g Jaydia lineatusd Auxis thazarde Gobiidaec,d,g Bregmacerotidaee,f Dendrochirus bellusd Coryphaena hippurusa Scorpaenidaed,g Diodon holocanthusg Apogonidaed,e,g Myctophidaeb,c,f Rhabdamia gracilisg Harpadon nehereusc Nettastomatidaec Total taxa: 57

16.76 10.25 9.79 7.88 7.31 6.64 5.41 3.91 3.63 3.63 3.55 3.45 3.01 2.67 2.63

Harpadon nehereusc Gobiidaec,d,g

59.16 40.84

Engraulis japonicuse Gobiidaec,d,g Engraulidaee,g Encrasicholina punctiferg Cynoglossidaed Ophichthidaec,d,g Atrobucca nibed Pennahia argentatac Bregmacerotidaee,f Myctophidaeb,c,f Auxis rocheie Decapterus macarellusf Harpadon nehereusc Pennahia anead Auxis thazarde Total taxa: 108

Total taxa: 7

a

Bathy-demersal fish. Bathy-pelagic fish. Bentho-pelagic fish. d Demersal fish. e Pelagic-neritic fish. f Pelagic-oceanic fish. g Reef-associated fish. b c

ichthyoplankton was observed in the Kuroshio-influenced area (offshore area), but its diversity index was much higher than that of the CDW-influenced area. In total, 34 taxa of fish eggs and 93 taxa of fish larvae were present in the Kuroshio-influenced area (Table 5). Therefore, we conclude that the inshore area of the ECS was more abundant in ichthyoplankton than the offshore area, whereas the converse was true for diversity. This phenomenon exists not only in the ECS ecosystem, but also in other large marine ecosystems (LME) such as the northern California Current LME of the Oregon coast (Auth and Brodeur, 2006) and the northeast United States Continental Shelf LME of North Carolina (Powell and Robbins, 1994). Because environmental conditions in the inshore area and estuary are more complex, with interaction between freshwater and seawater, fewer species are likely to tolerate this environment. In contrast, the increasing water temperature and salinity from the inshore to the offshore areas in the ECS, and from the NECS to the SECS, are suitable for most subtropical and tropical fishes. In addition, water depth increased from the inshore areas to the offshore areas, offering vast spaces and different niches for diverse species. Hence, diversity of adult fishes and ichthyoplankton is higher in offshore areas than inshore areas (Jin et al., 2003; Chang et al., 2012).

The distribution patterns of the fish egg assemblages were more related to the water masses than those of the fish larvae (Fig. 4), possibly because the distributions of fish eggs were mainly determined by selection of habitats and spawning grounds by adult fishes and by currents. In contrast, the distributions of fish larvae are influenced not only by the adult fishes and currents, but also by the swimming abilities of the larvae themselves for feeding and to avoid predators (Job and Bellwood, 2000; Paris and Cowen, 2004; Garrido et al., 2009; Staaterman and Paris, 2013). Moreover, the bipartite life cycle of fishes (Thresher et al., 1989; Franco et al., 2013) is another potential factor. For example, in this study, fish eggs and larvae belonging to five taxa—B. dussumieri, C. synderi, H. nehereus, P. anomala, and T. japonicus—tended to distribute in the Kuroshio- and CDW-influenced areas, respectively. Two other taxa —C. hippurus and S. tenuifilis—exhibited the opposite phenomenon for ichthyoplankton distributions. Furthermore, we observed that 24 taxa of fish larvae belonging to reef-associated fish were distributed in the Kuroshio-influenced area (Table 5), demonstrating that the adult fishes initially laid their eggs in inshore areas and, subsequently, the hatchlings migrated to offshore areas. This discrepancy might explain why distribution patterns of fish larvae assemblages did not fit well with the water masses.

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Fig. 6. CCV results of (a) fish eggs and (b) fish larvae in the East China Sea.

4.3. Why were there only 18 common taxa between fish egg and larvae compositions? In the study, a total of 150 taxa of ichthyoplankton were identified, accounting for 13.65% of the 1,099 species recorded for the ECS in FishBase. Fish eggs and larvae differed considerably in numbers of taxa and compositions. Overall, 124 taxa of fish larvae

were collected, which was 2.76 times the number of taxa of fish eggs (n ¼ 45), and only 18 taxa were common to both fish egg and fish larvae samples. This phenomenon has also been reported for other sea areas such as Camamu Bay of Brazil (Katsuragawa et al., 2011) and for the North and Northwest Iberian Peninsula (Rodriguez et al., 2009). We describe several possible explanations for this outcome below.

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Table 6 Results of CCA of fish eggs and larvae in the East China Sea ((a)–(g) represent the habitats of fishes). CDW-influenced group

Kuroshio-influenced group

Eggs

Coryphaena hippuruse Cynoglossus interruptusd Engraulis japonicuse Erisphex pottiid Harpadon nehereusc Minous monodactylusd Paraplagusia japonicad Seriola dumerilig Setipinna tenuifilise Sillago japonicad Strophidon satheteg

Acropoma japonicumb Auxis rocheie Brama dussumierie Champsodon snyderid Chauliodus sp.b Cheilopogon sp.e,f Coris pictag Cubiceps sp.c Cynoglossus areld Decapterus kurroidesg Diodon holocanthusg Erythrocles scintillansc

Euthynnus affinise Heteromycteris japonicusd Heteromycteris sp.d Lampris guttatusb Minous sp.d Muraenesox cinereusd Nemipterus virgatusd Ophichthidae gen. sp.1c,d,g Ophichthidae gen. sp.2c,d,g Ophichthidae gen. sp.3c,d,g Ophichthidae gen. sp.4c,d,g Plagiopsetta glossad

Psenopsis anomalac Samaridaed Saurida elongatad Synodus macropsd Thryssa sp.e Thunnus tonggole Trachinocephalus myopsg Trichiuridaec Trichiurus japonicusc Uranoscopus oligolepisd

Larvae

Atrobucca nibed Blenniidaec,d,g Brama dussumierie Brama sp.e Champsodon snyderid Cynoglossidaed Cynoglossus interruptusd Encrasicholina punctiferg Engraulidaee,g Engraulis japonicuse Gadidaed Gobiidaec,d,g Jaydia lineatusd Johnius sp.d Myctophidaeb,c,f Otolithes ruberc Pennahia anead Pennahia argentatac Psenopsis anomalac Repomucenus sp.d Sardinella melanurae Sciaenidaec,d,e Secutor ruconiusd Sillago sp.c,d,g Spratelloides gracilise Stomiidaeb,f Syngnathidaea,d,e,g Trichiuridaec Trichiurus japonicusc Valamugil sp.e Valenciennea wardiig

Abudefduf vaigiensisg Acanthurus nigrofuscusg Acropoma japonicumb Ammodytes sp.d Ammodytidaed Antennarius striatusg Apogon sp.d,e,g Apogonidaed,e,g Asterropteryx semipunctatag Aulotrachichthys prosthemiusb Auxis rocheie Auxis thazarde Balistapus undulatusg Bathycallionymus kaianusd Bothidaed Branchiostegus japonicusd Bregmaceros sp.e,f Bregmacerotidaee,f Carangidaec,d,e,f,g Carapidaeg Cephalopholis boenakg Chauliodus sloanib Chrysochir aureusc Clupeidaee,g Congridaea,d,g Coryphaena hippuruse Cyclothone pallidab Decapterus kurroidesg Decapterus macarellusf Decapterus sp.b,f,g Dendrochirus bellusd Diagramma pictag

Diaphus richardsonib Diodon holocanthusg Engyprosopon sp.g Euthynnus affinise Fistulariidaeg Gerreidaed,g Gobiesocidaed Gonostomatidaeb Halichoeres melanochirg Harpadon nehereusc Heteromycteris japonicusd Hoplolatilus marcosig Howella sp.b Labridaed,g Lagocephalus inermisd Lampanyctus alatusb Lampanyctus sp.b Leiognathidaed Lepidotrigla guentherid Lutjanus rufolineatusg Lutjanus russelliig Mene maculatag Minous sp.d Molidaef Moridaea Myersina filiferd Nemipterus virgatusd Neobythites sivicolac Nettastomatidaec Odontanthias borboniusf Onigocia spinosad Ophichthidaec,d,g

Paralepididaeb,f Pennahia sp.c,d Platycephalidaec,d,g Pleuronectidaed Polyductylus sextariusd Pomacentridaeg Priacanthus macracanthusg Rhabdamia gracilisg Rogadius sp.d Sarda orientalise Scarus ghobbang Scomber australasicuse Scomberoides sp.g Scombridaee,f,g Scopelarchidaeb Scorpaenidaed,g Scorpaenopsis venosag Serranidaea,c,d,g Setipinna tinuifilise Soleidaed,g Stephanolepis cirrhiferd Synodontidaec,d,e,g Synodus sp.d,g Terapon jarbuad Tetraodontidaec,d,g Trachinocephalus myopsg Upeneus japonicusg Vinciguerria sp.b Xiphias gladiusf

a

Bathy-demersal fish. Bathy-pelagic fish. c Bentho-pelagic fish. d Demersal fish. e Pelagic-neritic fish. f Pelagic-oceanic fish. g Reef-associated fish. b

The mesh size (1.0 mm) of the RMI net used in our study may have failed to capture some tiny fish eggs, given that most eggs of marine fish are between 0.5 and 2.0 mm in size. Even so, 68.33% of the eggs collected in the study were smaller than 1.0 mm, e.g., those of the top three dominant species, A. rochei (0.7 mm), C. arel (0.7 mm), and C. interruptus (0.6 mm). Abundant zooplankton inside the net may have blocked the mesh and prevented tiny eggs from passing through. Previous studies have indicated that a net with a small mesh size samples more plankton and species than one with a large mesh size (Colton et al., 1980; Hernandez et al., 2010). Unfortunately, samples obtained using nets with o1.0 mm mesh size were all fixed in formalin solution, and only the samples fixed in 95% ethanol solution (i.e. those collected from the net with 1.0 mm mesh size) could be used for DNA barcoding. Due to the fact that this study was part of the LORECS project and numerous physical, chemical, and geographic surveys had to be finished within 2 weeks under challenging weather and ocean conditions,

no further samplings could be performed. We recommend a mesh size of r0.5 mm for future ichthyoplankton surveys so that the results from these studies can be compared. Another potential reason for the discrepancy is that hatching times can differ among species. In general, most marine teleost fish hatch in 0.5–2 days when water temperatures are above 20 °C, but there are species with longer hatching times (Pauly and Pullin, 1988). Therefore, fish egg assemblages can be dissimilar to those of fish larvae. Different spawning types may also be a contributory factor. Although most marine fish lay pelagic eggs, some exceptions exist, e.g., gobies and damselfish lay demersal eggs, and several species of cardinal fish are mouth brooders. The eggs of these species cannot be collected using an ichthyoplankton net, but their larvae can (Shao et al., 2001). Another important reason may be that the sampling period of the study was not the main spawning period for the species whose larvae, but not eggs, were captured. Being aware of these potential biases, we compiled and

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analyzed data on both fish eggs and larvae to comprehensively understand the ichthyoplankton assemblages in the ECS. 4.4. Comparison with previous studies Few large-scale molecular identification studies have been conducted on ichthyoplankton in the ECS during the summer season. Since the survey area and season of the studies by Jiang et al. (2007) and Wan et al. (2014) are similar to this study, our results can be compared, despite the fact that their studies only performed morphological identification and mainly focused on the composition and biodiversity of ichthyoplankton without assemblage analyses. The two studies of Jiang et al. (2007) and Wan et al. (2014) were carried out from June to August of 1999 and from 13 to 21 June 2008, respectively. Jiang et al. (2007) reported that fish eggs were identified into 17 taxa and fish larvae into 70 taxa. The dominant species of fish eggs were Scomber japonicus, E. japonicus, and Priacanthus macracanthus, while the dominant species of fish larvae were E. japonicus and Euthynnus sp. Ten years later, Wan et al. (2014) collected 23 taxa of fish eggs and 32 taxa of fish larvae, with the dominant species being Stolephorus commersonii, Silliago sihama, and Cynoglossus joyneri for fish eggs and A. rochei, Upeneus bensasi, and E. japonicus for fish larvae. Comparison of the dominant species of ichthyoplankton shows that species of Engraulidae and Scombridae were abundant in all three studies, revealing summer to be the main spawning season for these species. The biodiversity of ichthyoplankton also showed similar trends among these three studies. Since the waters

in the offshore and SECS areas are warmer and deeper than the inshore or NECS areas, as well as offering more suitable habitats for temperate and tropical fish, most species of these three studies were found to be inhabitants of the offshore and SECS areas, which resulted in their greater biodiversity. The results from Jiang et al. (2007), Wan et al. (2014), and the current study demonstrate that species composition and biodiversity were similar among different summers. The composition and assemblages of fish larvae can also be compared with the findings of Chen et al. (2014), as part of the LORECS project, conducted fish larvae sampling during the winter and summer of 2008 but only used a morphological approach for identification. The dominant families common to the two studies are Apogonidae, Bregmacerotidae, Cynoglossidae, Engraulidae, Gobiidae, Myctophidae, Sciaenidae, Scombridae, and Synodontidae, indicating that summer is the main reproductive season for these families, amongst which the Engraulidae, E. japonicus, was most dominant. Comparing the nine common families in these two studies further revealed that more taxa were identified in this study, which used DNA barcoding. For instance, for the Engraulidae, we identified two more taxa than Chen et al. (2014); for the Sciaenidae, we identified four as opposed to none; and, for the Scombridae, we identified two taxa as opposed to one (Table 7). Overall, these results demonstrate that DNA barcoding provides more accurate data for understanding fish resources in the ECS. The dominant families in the winter sample of Chen et al. (2014) are clearly different to those of both their summer sample and the current study. Myctophidae, Mugilidae, Gonostomatidae, Scorpaenidae, and Scombridae, which accounted for over half of

Table 7 Comparison of dominant taxa of fish larvae in previous and current studies. Chen et al. (2014) (Winter 2008) Taxa Myctophidae Diaphus A group Diaphus B group Myctophum asperum Ceratoscopelus warmingii Mugilidae Valamugil sp. Gonostomatidae Sigmops gracilis Scorpaenidae Gobiidae Gobiidae type 2 Other Gobiidae species Callionymidae Trichiuridae Trachurus japonicus Trichiurus lepturus Bregmacerotidae Bregmaceros spp. Scombridae Scomber japonicus Scomber australasicus Triglidae Champsodontidae Champsodon sp. Phosichthyidae Vinciguerria nimbaria Teraponidae Others ( o1%, 165 taxa) Total

RA%: relative abundance (%).

RA%

Chen et al. (2014) (Summer 2008) Taxa

RA%

This study (Summer 2009) Taxa

RA%

14.52 6.33 4.95 1.77 1.47

Gobiidae Gobiidae type 1 Gobiidae type 2 Other Gobiidae species Synodontidae

28.43 8.03 17.69 2.71 19.39

Engraulidae Engraulidae sp.1 Engraulis japonicus Encrasicholina punctifer Gobiidae

32.11 4.57 26.03 1.51 12.94

14.23 14.23 10.46 10.46 10.06 5.80 3.09 2.71 4.85 4.36 2.93 1.43 2.61 2.61 2.27 1.25 1.02 1.72 1.51 1.51 1.25 1.25 1.09 25.27 100

Saurida spp. Trachinocephalus myops Engraulidae Engraulisjaponicus Bregmacerotidae Bregmaceros spp. Sciaenidae Cynoglossidae Cynoglossus spp. Apogonidae Myctophidae Benthosema pterotum Callionymidae Leiognathidae Scorpaenidae Scombridae Auxis sp. Others ( o 1%, 170 taxa) Total

17.16 2.23 11.07 11.07 6.81 6.81 5.76 5.39 5.39 2.66 2.29 2.29 1.77 1.29 1.11 1.03 1.03 13 100

Gobiidae sp.1 Valenciennea wardii Sciaenidae Atrobucca nibe Pennahia argentata Johnius sp. Pennahia anea Myctophidae Scombridae Auxis rochei Auxis thazard Ophichthidae Gadidae Cynoglossidae Carangidae Decapterus macarellus Bregmacerotidae Mugilidae Moolgarda sp. Trichiuridae Trichiurus japonicus Apogonidae Jaydia lineatus Synodontidae Harpadon nehereus Bramidae Brama dussumieri Others ( o 1%, 102 taxa) Total

10.13 2.81 12.04 4.44 3.83 2.42 1.35 8.13 3.51 2.36 1.15 2.41 2.18 2.16 1.61 1.61 1.52 1.50 1.50 1.28 1.28 1.27 1.27 1.22 1.22 1.04 1.04 15.08 100

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the fish larvae sample, were dominant in winter in the ECS (Table 7). With the exception of Gonostomatidae, these families also existed in the summer season, but were not as dominant. Importantly, the taxa within these families in winter also differed from the summer. These results emphasize the importance of identifying ichthyoplankton to the genus or species level so that a more precise understanding of spawning season can be achieved. In comparing the assemblage patterns in our study with those of Chen et al. (2014), we grouped our 25 stations into mixed-shore, northern-offshore, and CDW assemblages, whereas Chen et al. (2014) grouped the 16 stations of their summer sampling into summer coastal, offshore, and inshore assemblages. Despite these assemblages not being analogous, common dominant taxa are apparent. First, Chen et al. (2014) reported that the summer coastal assemblage mainly contained Gobiidae, Saurida spp., E. japonicus, Cynoglossidae, and Sciaenidae, which are the same taxa of the mixed-shore assemblage of our study. Second, the offshore assemblage in their study was chiefly distributed in the middle and southern parts of the ECS, which differs from the northernoffshore assemblage of this study. However, both assemblages largely consisted of pelagic-neritic fishes and certain reef-associated fish species. Finally, the distribution area of the Chen et al. (2014) inshore assemblage is equivalent to the area of transect E in our study, but slightly different from that of the CDW assemblage (transect F). The inshore assemblage in their study mainly included Scorpaenidae, Gobiidae, and Apogonidae, whereas transect E in our study mainly included species such as E. japonicus, A. nibe, Gobiidae, Valenciennea wardii, Cynoglossidae, D. macarellus, and Pennahia anea. The dominant species of the CDW assemblage in our study were H. nehereus and Gobiidae. This difference in these latter two assemblages is the main discrepancy between the two summer studies. In summary, the differences in summer assemblages between Chen et al. (2014) and our study may be because our study comprised more stations. In addition, we used DNA barcoding for identification, whereas they used morphology. Therefore, a higher number of stations and more accurate identification tools could have provided better resolution of species compositions in our study. Two assemblages, inshore and offshore, were grouped in the winter study of Chen et al. (2014). Although Chen et al. (2014) used the same assemblage names in their summer study, the names represented different stations in winter. The dominant winter inshore assemblage taxa in their study were Scorpaenidae, Gobiidae, and Trachurus japonicus, whereas Valamugil sp., Sigmops gracilis, and Diaphus A group were dominant in the winter offshore assemblage. These dominant taxa differ from those of the summer assemblages of Chen et al. (2014) and our study, so seasonal variation in species composition and assemblages exists in the ECS.

5. Conclusion In this study, we report the first results using DNA barcoding to identify fish eggs and larvae collected during a broad sampling in the ECS in the summer of 2009. A total of 8,933 fish eggs and 12,161 larvae were identified and classified into 45 and 124 taxa, respectively, overall representing 150 ichthyoplankton taxa and 13.65% of the 1,099 species recorded for the ECS in FishBase. Second, we discovered that the species compositions were dissimilar between the CDW-influenced and Kuroshio-influenced areas in the ECS, with these two water masses having different environmental features and covering different areas of the ECS. Few species could tolerate the complex environment of the CDWinfluenced area. The Kuroshio-influenced area featured warmer and deeper water, as well as offering vast space and different niches for diverse species, resulting in higher diversity than the

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CDW-influenced area. Third, we found that the assemblages of fish eggs fit well with water masses, whereas those of fish larvae did not. This scenario may be because fish larvae possess swimming abilities for feeding or to avoid predators and perhaps due to the bipartite life-cycle of fishes. Our study not only provides some insight into the spawning periods and spawning grounds of these 150 taxa, but also the relationship between the distribution of ichthyoplankton and environmental variables. This information will be useful for future ichthyological studies. More studies conducted in different seasons and with nets of smaller mesh size are needed in order to better understand the assemblages of ichthyoplankton in the ECS.

Data accessibility All sequences have been deposited in Genbank (Genbank accession numbers from KT718119 to KT718618). Specimens, collection data, sequences, and photos have been deposited in the Fish Database of Taiwan (http://fishdb.sinica.edu.tw/) with specimen vouchers from ASIZP0078466 to ASIZP0078956.

Acknowledgments This research was supported in part by the Ministry of Science and Technology, R.O.C. (NSC 98-2611-M-001-002). We appreciate the LORECS research team for the assistance provided in fieldwork and hydrographic data collection, and members of the Laboratory of Fish Ecology and Evolution, Biodiversity Research Center, Academia Sinica, who offered technical assistance in this study. We are also grateful to Ms. Lee-Sea Chen and Dr. John O’Brien for editing assistance.

Appendix A. Supplementary material Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.csr.2016.07.016.

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