Institute of Oceanology, Chinese Academy of Sciences
Article Information
- Bai Huaiyu, Wang Yukun, Zhang Tingting, Dai Fangqun, Huang Lingfeng, Sun Yao
- Determination of trophic levels of marine fish in the Yellow Sea and northern East China Sea using nitrogen stable isotope (δ15N) analysis of otoliths
- Journal of Oceanology and Limnology, 40(2): 634-642
- http://dx.doi.org/10.1007/s00343-021-0388-3
Article History
- Received Oct. 12, 2020
- accepted in principle Dec. 28, 2020
- accepted for publication Apr. 1, 2021
2 Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, China;
3 Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, Xiamen University, Xiamen 361005, China;
4 Laboratory for Marine Fisheries Science and Food Production Processes, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266071, China
Fishes are one of the most crucial species in the ocean that provide a rich source of nutrition, and economic income (e.g., seahorses as traditional Chinese medicine; cod-liver oil providing Vitamin A and E) for human beings. At the same time, they maintain stability and balance in marine ecosystems (Luong et al., 2020). The excessive exploitation of marine resources in the past decades has heightened the demand for the restoration of fishery resources and the protection of marine ecosystems. The interest in marine restoration has increased the importance of understanding fish functional roles in their ecosystems (Zhao et al., 2016). In addition, overfishing is likely to alter trophic levels in marine fishes (Greenstreet and Rogers, 2006; Maitra et al., 2018).
Trophic level (TL) refers to the trophic position in which one organism is placed in the food chain of an ecosystem. Marine ecosystems are complex and are supposed to have a structure of multiple trophic levels (Qu et al., 2016). Understanding trophic levels and their alteration of every kind of marine organisms, especially fishes, is one of the fundamental goals of modern marine ecology (Du et al., 2020). Traditionally, fish TLs were estimated by means of stomach content analysis (SCA). In this method, the fish gut is examined and the various taxa preyed upon by the fish are identified and quantified (e.g., Varela et al., 2018).
Stable isotope analysis (SIA) is another method of determining TLs by making use of the ratio 15N/14N, which increases with increasing trophic level and can be measured in fish tissue as described below (Greenstreet and Rogers, 2006; Varela et al., 2018; Du et al., 2020). SIA is a widely used approach in describing trophic interactions in natural systems and in defining time-integrated feeding relationships within an ecosystem (Ohshimo et al., 2019). It is also considered one of the most effective methods for determining trophic levels in any food web (Dame and Christian, 2008), and has become an important approach for investigating trophic interactions in food webs in the past few decades (Post, 2002). This method typically uses the white muscle tissue (WMT) because of the small amount needed for analysis (generally 1 mg) (Varela et al., 2018). However, the WMT δ15N (δ15Nwmt), which is commonly used for ecological studies, only provides a very limited temporal scope, in particular, a span of months to years for fishes (Madigan et al., 2012).
The otolith, which is one of the unique organs of teleosts, records information of the entire lifespan of most teleosts. Calcium carbonate precipitates from the endolymphatic fluid onto a protein matrix inside the otolith to form an aragonitic crystal. The protein matrix, which can regulate crystal growth, has an abundance of aspartic and glutamic acids and is partly glycosylated (Miller et al., 2006). New material is deposited daily on the outside edge of otolith which makes it ideal for dietary analysis (Elsdon et al., 2010). Fish gills sieve outside waters and absorb the elements present in it. These are then deposited to form the otoliths. Once deposited, they are rarely dissolved and also remain protected from the metabolism of the fish, which means changes seldom occur inside the otolith (Campana and Thorrold, 2001). Its metabolic inertness thus helps record ontogenetic information dating back to fishes' juvenile stage (Brown et al., 2019). For the afore-mentioned reason, scientists often use otolith chemistry to reconstruct the movement or origin of migratory fishes (e.g., the Australian bass, Percalates novemaculeata, Cameron et al., 2016). This reconstruction is made possible by the chemical "fingerprints" that estuaries, rivers, or oceanic areas leave on the otolith (Sturrock et al., 2012). Apart from that, otolith contains protein in the "organic matrix (OM)" (e.g., Miller et al., 2006), thus allowing stable nitrogen isotope analysis of the otolith. For instance, the otolith δ15N (δ15Noto) may allow for reconstruction of fish trophic history and changes in food webs (Lueders-Dumont et al., 2020).
The Yellow Sea and the East China Sea are important regions of commercial fisheries in China. Some of the most important commercial fishes are large yellow croaker (Pseudosciaena crocea), small yellow croaker (Larimichthys polyactis), and largehead hairtail (Trichiurus haumela); and important non-fish species include cuttlefish (Sepiella maindroni) and swimming crab (Portunus trituberculatus) (Zhao et al., 2016; Ma et al., 2019). In the past decades, fishery resources have declined due to overfishing and environmental destruction. Small and low-value fish account for more than 80% to 90% of the marine fish landings in the present (Zhao et al., 2016). The government of China has gradually recognized the seriousness of this problem. It has started programs aimed at both ecological restoration and protecting fishery resources (Liang et al., 2020). The information on TLs of marine fishes may assist in the formulation of management plans and serve as reference for conservation.
Previous research has found that δ15Noto and δ15Nwmt are highly correlated, which has been shown within individual species (Grønkjær et al., 2013; Lueders-Dumont et al., 2018) and also across a diversity of different species (Lueders-Dumont et al., 2020). The relationship between δ15Noto and δ15Nwmt for fish in the Yellow Sea and northern East China Sea has never been investigated as the previous study focused on aquaculture and Atlantic Ocean species. In the current study, we used an EA-IRMS (elemental analyzer coupled with an isotope ratio mass spectrometer), which is an instrument commonly used in ecological studies. In contrast, the previous study comparing δ15Noto and δ15Nwmt of multiple species (Lueders-Dumont et al. 2020) measured δ15Noto using a specialized method with limited availability in ecological laboratories.
The goals of the present study were to explore the scientific value of fish otolith δ15N (δ15Noto) for SIA and TL analysis, and to assess the potential of otolith as a standard specimen for marine fish TL analysis. We, specifically, aimed to answer the following questions while confirming and expanding previous findings: (1) is there any parallel correlation between δ15Noto and δ15Nwmt for the species in the current study? (2) how do TL estimates based on δ15Noto (TLoto) compare to TL estimates based on δ15Nwmt (TLwmt) from the same fish?
2 MATERIAL AND METHOD 2.1 Sample collectionThirty-six species of marine teleost fishes were collected from 2011 to 2014 in the Yellow Sea and northern East China Sea (30.0°N–39.1°N, 120.8°E–126.3°E) during the investigation of the R/V Beidou which belonged to the Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences. Both juvenile and adult fishes were selected randomly from the trawl survey sampling program. The sampled region covers a total area of 2.94×105 km2 (Fig. 1). We established a total of 104 sampling stations. The voyages were held during the autumn of 2011 (October and November), summer of 2013 (July and August), and summer of 2014 (July and August). The whole fishes were directly frozen upon collection. All the fishes were identified to species level while on the vessel. Three to five individual fishes were sampled for every species used in our analysis.
2.2 Otolith and WMT processingSagittal otoliths were removed with scalpel and forceps before being soaked in 30% hydrogen peroxide (H2O2) for 24 h to remove organic matter from otolith surfaces. The otoliths were then washed with distilled water before being dried. WMT was collected from the dorsal side of the fish with a scalpel. The otoliths and tissue samples were then dried at 60 ℃ for 48 h in the same clean drying oven. After that, otoliths and tissue samples were crushed and ground into powder using mortar and pestle for SIA. For a given species and measurement type (otoliths or WMT), multiple samples were combined to get a single measurement.
2.3 Stable isotope analysis (SIA)The SIA was conducted at the Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences. Sample powder (otoliths ~70 mg, WMT ~2 mg) was weighed into tin capsules before being analyzed using an EA-IRMS (IsoPrime 100, Elementar, UK) without being acidified. δ15N was calculated using the following equation:
R represents the ratio, 15N/14N; Rstandard is the δ15N of N2 in the air. The SIA was run such that for every five specimens analyzed, an analysis of the standard followed. The samples were calibrated with USGS40 (δ15N =-4.52‰±0.06‰) and USGS41a (δ15N=47.55‰ ±0.15‰). For every 10 specimens, one specimen was randomly selected for repeat analysis of 2 or 3 times. The precision of the instrument was 0.2‰ for δ15Nwmt and 0.3‰ for δ15Noto.
2.4 Trophic level estimationThe TLs of fishes were estimated from δ15Nwmt using the formula suggested in Cai et al. (2005):
The value 2.5 refers to the trophic enrichment factor (in ‰), and δ15Nbaseline refers to the baseline δ15N used for the estimation of trophic level. δ15Nbaseline in the formula of Cai et al. (2005) was 6.05‰, which was the δ15N of Mytilus edulis. M. edulis is a mussel species present in the Yellow and East China Seas. Since the current research was more of a theoretical exercise, where the authors were interested in roughly comparing otolith-derived TL to muscle-derived TL and we had identical sampling areas and similar research aims as Cai et al. (2005), no extra baseline δ15N was measured here.
2.5 Data analysisWe performed Pearson Correlation Analysis to test for the association between paired δ15Noto and δ15Nwmt samples and the strength of the relationship between the two variables. Regression analysis was used to investigate the slope, y-intercept, and variability between δ15Noto and δ15Nwmt. The most suitable regression relation was selected from 11 regression models in SPSS and the results were subsequently used in the TLoto formula (Eq.4). Statistical analyses were conducted using Excel 2013 and SPSS 19.0.
3 RESULT 3.1 δ15N analysisδ15Nwmt ranged from 10.4‰ (Miichthys miiuy) to 14.4‰ (Nibea albiflora) on average (mean±SE) of 12.2‰±0.9‰. δ15Noto ranged from 9.2‰ (Chelidonichthys kumu) to 15.4‰ (Synechogobius hasta), with an average (mean±SE) of 11.7‰±1.4‰. Ten of the 36 fishes had a lower δ15Nwmt than δ15Noto, and the other 26 species had a higher δ15Nwmt than δ15Noto. Our findings doubled the number of species for which this relationship has been investigated. Differences (δ15Noto–δ15Nwmt) ranged from -2.5‰ (Collichthys lucidus) to1.8‰ (S. hasta), with an average of -0.4‰. Species with high δ15Nwmt values like S. hasta and N. albiflora also obtained high δ15Noto. Others such as M. miiuy and C. kumu had low values in both (Table 1). δ13C data for WMT and otoliths can be found in Supplementary Table S1.
Pearson correlation analysis results (R=0.780, P < 0.001) showed that δ15Nwmt and δ15Noto had a highly significant positive correlation. We applied the result of linear regression analysis (Fig. 2) to the following trophic level analysis. The regression analysis for δ15Nwmt versus δ15Noto is:
To calculate TLoto, we combined Eq.2 with Eq.3:
The results of TLwmt and TLoto calculations are shown in Fig. 3. TLwmt ranged from 2.75 (M. miiuy) to 4.34 (N. albiflora), and TLoto ranged from 2.98 (C. kumu) to 4.18 (S. hasta). Due to the regression coefficient (δ15Nwmt vs. δ15Noto) used in the TL calculation, half (eighteen) of the thirty-six species investigated in the current experiment obtained a higher TLoto than TLwmt, and the differences in values (TLoto–TLwmt) ranged from 0.03 (S. tumbil) to 0.46 (P. argenteus). In contrast, 17 species had lower TLoto than TLwmt. The differences in values (TLoto– TLwmt) ranged from -0.06 (P. major & C. robustus) to -0.40 (J. belengerii). Only one species (L. polyactis) had the same TLwmt and TLoto value, which was 3.30. Species with high TLwmt values like N. albiflora and S. hasta also obtained high TLoto. Others such as M. miiuy and C. kumu had low values in both.
4 DISCUSSION 4.1 δ15N analysisAs the N content in the otolith was very low in our samples, we combined multiple otoliths (otolith powder ~70 mg) that was prepared to obtain a single measurement for SIA. In contrast, Lueders-Dumont et al. (2018) used only 2–4 mg of powder for the δ15N analysis of the modern and fossil otolith of the Atlantic cod, Gadus morhua. These differences in the weight of otolith powder could be due to the different SIA methods employed. Some other methods reduced the minimum mass of the otoliths required for analysis to as low as 2 mg (Cheng et al., 2018; Lueders-Dumont et al., 2018), unlike the methods in our study.
We compared the regression result with that of Lueders-Dumont et al. (2020) in which the slope of the line was close to 1 and the y-intercept was indistinguishable from 0. Although the dependent and independent variables were switched in our study (i.e. Eq.3; Fig. 2) aiming to get the TLoto equation (Eq.4), the slope and y-intercept were different from Lueders-Dumont et al. (2020) even if the two variables were switched. It could be related to the specific species used in each study, or to differences in age and life history stage between the two studies because only large, commercially-harvested fish species were used in the 2020 study.
Fish muscle tissue has a turn-over of only several months, which is mainly dependent on metabolism (Mohan et al., 2016). Therefore, a more recent life history information is recorded in WMT. In contrast, the otolith is continuously accruing new material; hence, it becomes a record of the entire life history of the fish. In our study, some δ15N components in the otolith were from the fish's early life whereas δ15N in the WMT recorded recent δ15N deposition. Thus, most species (26 species) had higher δ15Nwmt than δ15Noto. However, the other 10 species had lower δ15Nwmt. The reason could be diet shift in their life history which had changed their δ15Nwmt values, and otolith surface was also increasing continuously as the otolith grew which led to the possibility that the otolith mass was, like WMT, "weighted" toward the recent life history.
δ15Nwmt and δ15Noto should not necessarily be identical as different amino acids and proteins can be used in the development of different fish body components (McMahon et al., 2011; Lueders-Dumont et al., 2018). Differences in δ15Nwmt and δ15Noto values could also be attributed to the differences in amino acid concentrations and in the degree of amino acid routing to various tissues (Macko et al., 1986; McMahon et al., 2010; Mohan et al., 2016). Moreover, differences arose across different fish species. Lueders-Dumont et al. (2020) found that fish producing large otolith tended to have δ15Nwmt > δ15Noto whereas fish producing small otoliths tended to have δ15Nwmt < δ15Noto. Fishes with small otoliths (e.g., Clupea pallasi) in our study, had lower δ15Nwmt compared to δ15Noto, and fishes with large otoliths (e.g., Johnius belengerii, Gadus macrocephalus) showed higher δ15Nwmt than δ15Noto, which was similar to the otolith-size-based results from Lueders-Dumont et al. (2020).
4.2 TL estimationWe used the TL Eq.2 and δ15Nbaseline from Cai et al. (2005). We found that these were applicable in our case since both studies focused on the same areas and on fishes. Furthermore, this formula and parameters were obtained based on a long-term study. However, Wan et al. (2010) argued that the research results of Cai et al. (2005) followed a study of a lake food web (Vander Zanden et al., 1997), which might be less applicable to pelagic areas. Nevertheless, Cai et al. (2005) used the δ15N baseline value (δ15Nbaseline) for the estimation of TL from the common mussel, M. edulis, which is a representative marine species in our study areas.
We wished to compare δ15N and TL data to previously published data. Converting δ15Noto to δ15Nwmt was the way to achieve this comparison. About 53% (19 species) of the 36 species' TLwmt and TLoto showed congruence within the TL range of ±5%, and 89% (32 species) of them showed congruence within the TL range of ±10%. Only one species, Pampus argenteus (16.19%), was out of the TL range of ±15%. This suggests that δ15Noto is a feasible technique for characterizing the TLs of marine fishes. δ15Noto provides a record of the whole life history whereas δ15Nwmt records the very recent δ15N (Section 4.1). Fishes tend to prey on larger prey from higher TLs as they grow. As a consequence, TLoto reflects the average TL of the entire life history. Therefore, TLwmt can be assumed to be higher than TLoto for any fish. However, this was not the case for 18 species which had lower TLwmt than TLoto (Section 3.2). This could be due to the fact that some species hunt different prey over their whole life history. As a result, they obtain different δ15N in their various growth stages. Environmental and ecological changes can also cause change in prey availability, which, in turn, can impact the consumer's δ15N. Many species experience diet shifts, even sharp shifts, in their lives like Ethmalosa fimbriata, Sarotherodon melanotheron, Gadus chalcogrammus, and Mallotus villosus (Gning et al., 2008; Marsh et al., 2017). Thus, the inconsistent changes in the TLwmt and TLoto values could be explained by the potential influence that diet shifts have on δ15N. Lueders-Dumont et al. (2020) argued that phylogeny and life history were not the main cause for the observed variation in the difference between δ15Nwmt and δ15Noto, which stood for TLwmt and TLoto. The discrepant point of view from ours could be due to different study areas and different target species.
Different TL values suggest predator-prey relationships in the food web (Qu et al., 2016; Du et al., 2020). Based on our study, N. albiflora and S. hasta (fishes with the highest TLwmt and TLoto values, respectively) are top predators, while M. miiuy and C. kumu (fishes with the lowest TLwmt and TLoto values, respectively) could be prey fishes.
We compared our TL estimation results of 18 species to those of Cai et al. (2005). We found that the TLwmt of M. miiuy and S. niphonius declined in the past several years. There might be two reasons for this. First, the increasing fishing pressure reduced the landing sizes of commercial fishes, and smaller sizes meant lower TLs. Pauly et al. (1998) once described a process, "Fishing down marine food webs". The observations in the current study were consistent with results showing changes to the trophic structure of marine food webs. Second, we were uncertain if all fish samples were adult individuals. The latter can influence our TL estimation since adults and juveniles can be expected to have different TL values.
Some of the species had raised TLwmt like H. nehereus and S. hasta. We could not ignore that human activities, especially overfishing, have been impacting the marine ecosystem (Greenstreet and Rogers, 2006; Luong et al., 2020). The dominant species which used to be top predators in a system are constantly replaced along with the changes in the food web. If top predators have decreased, it is possible that prey species could have increased population sizes. Moreover, decreased densities of top predators in the ecosystem could lead to the prey fish species growing to larger sizes (Cai et al., 2005). As a result, the TLs of prey species increase.
4.3 Future application of δ15Noto and TLotoThe nitrogen concentration of otolith-bound OM in our specimens is very low. The utility of δ15Noto analysis is limited by the low N content of otoliths. Highly sensitive analytical instruments or methods (e.g., peroxodisulphate oxidation-bacterial conversion method, Cheng et al., 2018) are needed to allow processing of smaller quantities that can provide reliable results. Our results show that measuring otoliths (~70 mg) is nonetheless possible using EAIRMS techniques.
Grønkjær et al. (2013) found that separating the soluble from insoluble organic matter resulted in different δ15N results. The comparison of the soluble vs. insoluble δ15N patterns could be another future direction, to examine whether the relationship between δ15Noto and δ15Nwmt depends upon using soluble, insoluble, or bulk organic matter in future studies.
In addition, otoliths do not decompose easily compared to WMT. Fossilized otoliths in sedimentary deposits can be specimens that record information of fishes in the past, or even that of ancient fishes (Lueders-Dumont et al., 2018).
In summary, otoliths are useful repositories of information that can be useful for ecological investigations, including trophic level estimation or the determination of differences in baseline experienced by different species or groups of the same species.
5 CONCLUSIONA total of 36 species of marine fishes were sampled from the Yellow Sea and northern East China Sea in 2011–2014. Our results show that there was a positive and highly significant correlation (R=0.780, P < 0.001) between δ15Noto and δ15Nwmt values of the fishes, of which 89% showed congruence with a TLoto range that fell within ±10% of TLwmt. TLwmt had changed through recent decades compared with previously published research. The use of δ15Noto is a feasible technique in characterizing the TLs of marine fishes and could therefore be used in marine ecosystem studies. We expected that more applications can be derived from the δ15Noto and TLoto techniques in the future.
6 DATA AVAILABILITY STATEMENTThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
7 ACKNOWLEDGMENTWe are grateful to Xijie ZHOU and Bin XIE of Xiamen University for their recommendations in the design of the research. We appreciate Qian YANG and Hongxia QIU of Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences for their help in the process of specimens. We also would like to express our gratitude to the crew of Beidou fishery research vessel.
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