1 Introduction
Carbon and nitrogen dynamics in the oceans are globally changing as a result of anthropogenic activities (Gruber and Galloway, 2008). Carbon and nitrogen isotope values [δ13C and δ15N, the differences from the sample isotope ratios (13C/12C and 15N/14N) to standard materials] are particularly effective measurements for detecting changes in marine environments (Gruber et al., 1999; Ren et al., 2017) and ecosystems (Lorrain et al., 2020). Earth system model-based techniques have been developed at a global scale in order to comprehend the spatiotemporal variability of carbon and nitrogen isotope values (Buchanan et al., 2019). Seas that connect land and ocean are strongly affected by anthropogenic activities (Omstedt, 2021). However, because current Earth system models do not focus on marginal seas, relevant information is extremely limited.
The Sea of Japan (SOJ) is a western North Pacific semi-closed marginal sea surrounded by the Korean Peninsula, the Japanese Archipelago, and the Russian coast. In the southern part of the SOJ, the Tsushima Warm Current (TWC) flows at the surface (< 200 m depth) from the west (East China Sea, ECS) to the east (western North Pacific or the Sea of Okhotsk) throughout the year; however, this current is weak in the winter and strong in the summer, and its path exhibits complex variations (Yabe et al., 2021). The TWC governs the elemental cycles and ecosystem in the southern part of the SOJ (Kodama, 2020), and its water is a mixture of the Kuroshio Current, the Taiwan Warm Current, and Changjiang discharged waters (Isobe, 1999; Guo et al., 2006). The elemental cycles in the surface layer of the SOJ are rapidly changing, and the pH, phosphate, and oxygen concentrations have been decreasing over the past five decades (Ono, 2021; Kodama et al., 2016; Ishizu et al., 2019).
Studies in the SOJ have been undertaken using a stable isotope ratio of zooplankton and small pelagic fish tissues to determine changes in carbon and nitrogen dynamics (Nakamura et al., 2022; Ohshimo et al., 2021). These studies found that the δ13C in animal tissues declined at the same rate as the Suess effect, but there was no significant linear trend in the δ15N (Nakamura et al., 2022; Ohshimo et al., 2021). The δ15N in animal tissues varies with trophic position. In the SOJ, the zooplankton biomass has been found to be negatively coupled with small pelagic fish biomass over the last half-century, and hence, food-web structure may periodically change (Kodama et al., 2022a). Thus, the baseline δ15N in marine ecosystems is vital for identifying changes in nitrogen dynamics in the SOJ. However, reports on the variation in stable isotope ratios of particulate organic matter (POM) in the surface layer, which are the baseline values of marine ecosystems, are confined to the Japanese coastal areas (Antonio et al., 2012; Nakamura et al., 2022). As a result, the variabilities in POM stable isotope ratios with regard to environmental parameters are not clearly understood.
Two recent studies have indicated that the carbon and nitrogen stable isotope ratio of POM varies in the western North Pacific marginal seas (Ho et al., 2021; Kodama et al., 2021). The δ13C of POM increased as the phytoplankton abundance increased in the southwestern ECS, which is consistent with isotope fractionation that occurs during photosynthesis (Ho et al., 2021). The δ15N of this area decreased with freshwater intake and coastal upwelling in the summer, and was negatively correlated with nitrate concentration (Ho et al., 2021). Gao et al. (2014) reported the horizontal distribution of δ13C and δ15N, and that δ13C largely varies in this sea. Active nitrogen fixation decreases the δ15N of POM in offshore waters in the Kuroshio area during the summer, whereas the δ15N of POM remains high in the coastal waters (Kodama et al., 2021).
The carbon and nitrogen isotope ratios of POM are thought to be more changeable in the SOJ than in the other western North Pacific marginal seas. In the case of carbon, Kosugi et al. (2016) reported that the partial pressure of CO2 (pCO2) on the surface of the central and eastern parts of the SOJ (312–329 µatm) differs from that in the northwestern region (360–380 µatm). Furthermore, the carbon:nitrogen ratio (C:N ratio) of POM in the surface water of the northern ECS is extremely high (>40:1) near Japan (Gao et al., 2014), and this organic carbon-rich water may influence the carbon dynamics of the SOJ. In the case of nitrogen, primary production in the northeastern ECS is supported by a variety of nitrogen sources, such as atmospheric deposition, Changjiang River discharge, and Kuroshio waters with varying δ15N (Umezawa et al., 2021; Umezawa et al., 2014). As the decrease in phosphate concentration in the SOJ is mainly observed with an increase in the nitrogen supply in the ECS (Kodama et al., 2016; Kim et al., 2013), advection of nitrate and POM produced in the northeastern ECS is likely to influence the δ15Ns in the SOJ. Previous nutrient dynamics studies have revealed that the supplied nitrate in the surface productive layer in the SOJ originates from the Kuroshio, regeneration at the bottom of the ECS, the Changjiang diluted waters, and the atmosphere in the summer (Kim et al., 2011; Kodama et al., 2017; Kodama et al., 2015). In addition, deep mixing occurs in the SOJ in winter (Ohishi et al., 2019); thus, deep-seawater-originated nitrate contributes to the primary production as well. Although their contributions to the plankton community are limited, diazotrophs are present during the summer (Hashimoto et al., 2012; Sato et al., 2021). Thus, the δ15N of POM helps in identifying which nitrogen source mainly supports primary production in this sea. Therefore, in this study, we investigated the carbon and nitrogen stable isotope ratios in the southern SOJ (approximately ≤ 40°N). The main objectives were to: 1) demonstrate the spatial distribution of carbon and nitrogen stable isotope ratios, 2) understand the effects of horizontal advection of particulate organic carbon from the ECS, and 3) identify which nitrogen source mainly supports the primary production.
2 Materials and Methods
2.1 Sampling
POM samples with environmental data were collected from 2016 to 2021 in the southern part of the SOJ between 35°N and 41°N and between 131°15'E and 139°50'E (Fig. 1) during 28 cruises using R/V
Hokko-maru,
Shunyo-Maru,
Yoko-Maru, T/V
Tenyo-Maru (Japan Fisheries Research and Education Agency), and R/V
Dai-Go Kaiyo-Maru (Kaiyo Engineering Co., Ltd.). The cruises were held yearly from February to September, except for March (Fig. 1). The observations were repeated 14 times after 2018 along the monitoring line established off the Sado Island (SI-line) (Fig. 1) (in September 2016, July 2018, August 2018, September 2018, February 2019, April 2019, June 2019, September 2019, February 2020, April 2020, June 2020, September 2020, February 2021, April 2021, and June 2021).
POM samples were generally obtained at the depths of 10 m and 30 m, or at the subsurface chlorophyll-a maximum (SCM). The vertical profiles of temperature, salinity, and chlorophyll-a fluorescence were monitored in real-time using a conductivity-temperature-depth (CTD) sensor (SBE9plus, Seabird Electronics). Real-time observations were not performed in some specific cases; here, the record-type CTD (SBE19, Seabird Electronics) without a fluorometer was used instead of the SBE9plus. On this basis, we set 30 m as the representative subsurface layer for the spring (March–June) and summer (July–September) seasons as it is below the surface mixed layer in SOJ. During the cruise in September 2020, the POM samples were collected at six depths (0, 10, 30, 50, and 100 m, and SCM) to determine the variations between the sampling layers. In the July and August cruises, photosynthetically active radiation (PAR) was occasionally accessible using a PAR sensor (Seabird Electronics) attached to the CTD. In the summer of SOJ, the mean (± SD) euphotic zone depth—where PAR is 1% of the surface—was 50 (± 14) m (n = 207), and the PAR at a depth of 30 m was 6.8 ± 4.3% of the surface. In May and August 2020, we collected samples at depths of 75 m (n = 3) and 100 m (n = 1), respectively. In spring and summer, considering the mixed layer and the euphotic zone depth, the layers at 0–10 m, 20–65 m, and 75–100 m depths were defined as the surface, subsurface, and deep layers, respectively, to detect the influence of the vertical difference of unevaluated characteristics such as light and phytoplankton community. In winter, all the samples were considered as surface layer samples because the difference in potential density between 10 and 30 m depths was <0.125 at every station, suggesting that they were within the mixed layer.
Temperature, salinity, nutrients (nitrate, nitrite, silicate, and phosphate), and chlorophyll-a concentrations were collected and utilized as environmental parameters at the same depth as the POM samples. Water samples were obtained using Niskin bottles mounted on a rosette or a bucket to measure nutrient, chlorophyll-a, and POM concentrations. The nutrient and chlorophyll-a concentrations were analyzed following Kodama et al. (2015). The detection limits of the nutrient concentrations were 0.01–<0.05 µM for nitrite and phosphate, 0.01–<0.1 µM for nitrate, and 0.05–<0.3 µM for silicate, estimated using the standard deviations of blank values. The temperature and salinity at the 0-m depth were defined as the values observed by the CTD sensors at 1-m depth because salinity at the 0 m depth sometimes exhibited “unreliable” values. To treat the common-logarithm transformed values, the nutrient concentrations of < 0.01 µM (below the detection limit) were set as 0.01 µM.
2.2 Isotope analyses
To measure the mass and the carbon and nitrogen stable isotope ratios of the POM, 0.9–11.5 L of seawater was filtered using a pre-combusted (450°C, 6 h) glass fiber filter (pore size: 0.7 µm, GF/F, Whatman). The filtration was stopped in 2 h after sampling, and > 5 L seawater was filtered in 2 h for most of the samples. After the shipboard filtration, the samples were frozen (< –20°C). Onshore laboratory analyses were performed using methods from Kodama et al. (2021), whose preservation differed from that of Lorrain et al. (2003), while the other protocols remained the same. The carbon and nitrogen isotope ratios and their masses were determined using the same sample. The samples were exposed to HCl fumes for > 2 h to remove carbonate salt, dried in an oven (60°C) overnight and stored in a desiccator until isotope ratio measurements. After another round of oven-drying, the entire glass filter was wrapped in a tin cup. Subsequently, the carbon and nitrogen isotope ratios were measured using an Isoprime 100 isotope ratio mass spectrometer (Elementar, Langenselbold, Germany). In 2016, the colored surfaces of the glass filters were scraped off, and the carbon and nitrogen isotope ratios were measured. The absolute amounts of carbon and nitrogen in these scraped samples were different from those in the whole samples. The stable isotope ratios of carbon and nitrogen were calibrated using curves obtained from L-alanine (Shoko Science) during the measurements. The isotope compositions of carbon and nitrogen of POM sample (δ13CPOM and δ15NPOM, respectively) were expressed using Eq. (1):
δ13CPOM or δ15NPOM = (Rsample /Rreference – 1.0) × 1000, (1) where Rsample, and Rreference are the heavy (13C and 15N) to light (12C and 14N) isotope ratios of the POM sample and reference, respectively. The reference materials were atmospheric N2 for nitrogen and Vienna Pee Dee Belemnite for carbon. Given the quality of the L-alanine, and δ13C and δ15N were rounded off to one decimal place, the precision of the analyses was within 0.2‰.
The C:N ratio of POM occasionally exhibited outliers, due to which, the mean and standard deviations (SD) of the C:N ratio were calculated and the samples whose C:N ratio differed by > 3 × SD from the mean value were eliminated. After elimination, we re-calculated the mean and SD of the C:N ratio, and the samples whose C:N ratio differed by > 3 × recalculated SD from the recalculated mean value were eliminated. Then, the samples lacking environmental data were removed. As a result, six samples were removed, and 507 samples were used in this study. Among these samples, 101 samples were obtained along the SI-line.
2.3 Statistical analyses
All statistical analyses were conducted using R (R Core Team, 2023). Accompanied by the analysis of variance (ANOVA), a pairwise test with the Tukey–Kramer adjacent was applied to the least-squared mean (lsmean) values. The “ggeffect” package (Lüdecke, 2018) was used to calculate the lsmean values and SEs. The “car” package (Fox and Weisberg, 2018) of ANOVA was used to conduct type II ANOVA for unbalanced data comparison. Neither δ13CPOM nor δ15NPOM exhibited a normal distribution according to the Kolmogorov–Smirnov test (p < 0.001); both showed multimodal distributions. Therefore, we applied a two-dimensional Gaussian mixed model (GMM) to classify δ13CPOM and δ15NPOM using the “mclust” package (Scrucca et al., 2016). The samples were not divided based on the sampling depth and seasons. The number of classes was determined using the Bayesian information criterion (BIC).
Generalized linear models (GLMs) were applied to assess the variables that predicted the relationships between the environmental parameters and δ13CPOM and δ15NPOM as in Kodama et al. (2021). We assumed that the error distributions of δ13CPOM and δ15NPOM were normally distributed with a linear link function in the GLMs. The full GLM models are as follows:
δXPOM ~ glm(f(class) + f(layer) + f(season) + Lon + Lat + C/N + T + S + Chl + Nit] (2) where δXPOM, Lon, Lat, C/N, T, S, Chl, and Nit denote δ13CPOM or δ15NPOM, longitude, latitude, C:N ratio, temperature, salinity, chlorophyll-a concentration, and nitrate concentration, respectively. The chlorophyll-a and nitrate concentrations were transformed into logarithmic values. The arguments of the f functions are categorical variables that are used to simulate non-linear relationships. The numbers of classes were defined by GMM and BIC, while those of layer and season were three each (surface, subsurface, and 100-m depth in layer and winter [January–February], spring [March–June], and summer [July–September] in season). The explanatory variables used in the full GLMs were chosen based on the retraction of multicollinearity. We also used GLM approaches that included a quadratic expression in the model, as Nakamura et al. (2022) did, as well as a generalized additive model approach, as Kodama et al. (2021) did. However, as the deviance explained values of the models did not improve, we opted for the simple GLM approach.
The explanatory variables and final GLM descriptions were selected using the corrected Akaike information criterion (AIC), which may be used to assess the likelihood of the model. The lsmean values and SEs based on the AIC-selected GLMs were used to visualize the influence of the explanatory variables on the δ13CPOM or δ15NPOM. ANOVA was used to test the effects of the lsmean values of the categorical variables (the class, season, and depth when they remained).
Of note, we did not include the interaction terms in the GLMs. For instance, longitude–latitude interactions can reflect the 2-D variations. Moreover, the temperature–salinity (T–S) diagram is the most fundamental method for obtaining the characteristics of water masses. Therefore, although the interactions must be considered, the interaction terms of temperature and salinity could not be used instead of the T–S diagram in the GLMs. Instead of interaction terms, the classes based on the GMM were plotted on the T–S and nitrate-density (σt) (N-σt) diagrams to elucidate the effect of non-linear interactions on δ13CPOM or δ15NPOM.
3 Results
3.1 Spatial distribution of δ13CPOM and δ15NPOM
The δ13CPOM and δ15NPOM varied from –29.3 to –17.7‰ and –3.2 to 6.7‰, respectively. In September 2020, the vertical profiles (0, 10, 30, 50, and 100 m depths and SCM) of δ13CPOM and δ15NPOM were collected at nine stations. SCM was observed at depths of 34–56 m. The differences in the δ13CPOM, δ15NPOM, and C:N ratios were significant among the layers according to ANOVA (p < 0.001). The δ13CPOM was the lowest at a depth of 50 m or SCM (mean ± SD: -25.7 ± 0.36‰ and –25.4 ± 0.45‰, respectively) (Fig. 2a). The δ13CPOM decreased with depth of up to 50 m (or SCM layer) and slightly increased at the 100-m depth (–24.2 ± 1.26‰). The δ15NPOM was the lowest at 10-m depth (1.5 ± 0.64‰), increased with depth, and the highest value was identified at the 100-m depth (5.0 ± 0.60‰) (Fig. 2b). The mean C:N ratio was estimated to 5.4–6.3 mol mol-1 except the 100-m depth with 4.2 ± 0.69 mol mol-1 (Fig. 2c). When the subsamples at the nine stations were regrouped into the surface (0 and 10 m depths) and subsurface (30 and 50 m and SCM) groups, the differences in both δ13CPOM and δ15NPOM were significant between the surface and subsurface (t-test, p ≤ 0.018). Therefore, δ13CPOM and δ15NPOM were different between the surface and subsurface, particularly during summer.
The horizontal variations in δ13CPOM are shown by the 1° × 1° grid median values (Fig. 3a–e). During winter, the median (± interquartile range, IQR) of the δ13CPOM was –25.0 ± 2.34‰, and low δ13CPOM was observed in the offshore waters (median ± IQR: –25.6 ± 3.20‰). During spring, low δ13CPOM was identified offshore, and the surface δ13CPOM (–24.6 ± 1.55‰) was lower than that of the subsurface (–23.7 ± 1.68‰, Wilcoxon test, W = 271, p = 0.021). During summer, the surface δ13CPOM (–22.9 ± 1.07‰) was significantly higher than that of the subsurface (–24.4 ± 1.86‰, Wilcoxon test, W = 36002, p < 0.001). The δ13CPOM in the subsurface during the summer was higher in the western than in the eastern part, while those of the surface were higher in the eastern than in the western part.
The horizontal variations in δ15NPOM are shown in the same manner as δ13CPOM (Fig. 3f–i). Evidently, in winter, lower values (~ –2.0‰) were observed in the coastal area. δ15NPOM was higher in spring than in winter (3.12 ± 2.02‰ and 3.12 ± 2.02‰ in the surface and subsurface, respectively). Moreover, during spring, the δ15NPOM value in the offshore area was lower than that of the coastal area. In summer, the surface δ15NPOM (2.76 ± 1.68‰) was significantly lower than that of the subsurface (3.14 ± 1.39‰, Wilcoxon test, W = 19187, p = 0.02874). Furthermore, lower δ15NPOM values were identified in the offshore water, and high δ15NPOM values were observed in the coastal area of the northern part.
3.2 Temporal variations of δ13CPOM and δ15NPOM along the SI-line
The monthly variations in δ13CPOM and δ15NPOM were evaluated using the samples collected along the SI-line (Fig. 4a–d). When the interannual variations were ignored, δ13CPOM was the lowest in April (lsmean ± SE: –26.1 ± 0.62‰) and highest in September (–22.4 ± 0.32‰) (Fig. 4a); no significant difference was observed during February, April, and June (Pairwise test with Tukey–Kramer adjacent, p > 0.05). The δ15NPOM in the surface layer was the lowest in February (–0.5 ± 0.40‰) and highest in June (2.7 ± 0.52‰), while a significant difference was not observed among June, July, and September (Fig. 4b). In the subsurface layer, the monthly variation of δ13CPOM was not significant (ANOVA, p = 0.09) (Fig. 4c), and that of δ15NPOM was significant (p = 0.026) but was not significant as per the pairwise test with Tukey–Kramer adjacent (p > 0.05) (Fig. 4d).
The surface δ13CPOM and δ15NPOM from July–September exhibited significant interannual variation (ANOVA, p < 0.05). The significant difference in interannual lsmean δ13CPOM (pairwise test with Tukey–Kramer adjacent, p = 0.036) was observed between 2019 (highest: –21.9 ± 0.35‰) and 2018 (lowest: –23.5 ± 0.35‰). The interannual lsmean δ15NPOM was the lowest in 2019 (1.5 ± 0.31‰), and significantly different (pairwise test with Tukey–Kramer adjacent, p = 0.016) from that in 2018 (3.0 ± 0.36‰). In the other combinations, there was no significant difference in the pairwise test. The temperature, salinity, and chlorophyll-a concentration in the surface layer during summer did not vary significantly (ANOVA, p > 0.1).
3.3 Classifications of δ13CPOM and δ15NPOM
Furthermore, δ13CPOM and δ15NPOM were divided into four classes (I–IV) according to the two-dimensional GMM (Fig. 5). The mean ± SDs of δ13CPOM and δ15NPOM were –24.6 ± 1.2‰ and –2.1 ± 0.8‰ in class I (n = 11), –23.7 ± 1.2‰ and 3.1 ± 1.2‰ in class II (n = 441), –27.1 ± 1.0‰ and 2.0 ± 1.3‰ in class III (n = 21), and –20.7 ± 0.8‰ and 1.7 ± 1.0‰ in class IV (n = 34), respectively (Fig. 5b–c). The δ13CPOM values were significantly different among the different classes (p < 0.001, pairwise test with Tukey–Kramer adjacent), except between classes I and II (p = 0.06) (Fig. 5b). The δ15NPOM values were significantly different among the different classes (p < 0.001, pairwise test with Tukey–Kramer adjacent), except between classes III and IV (p = 0.76) (Fig. 5c). However, the mean ± SD values of δ15NPOM in classes III and IV were within the range of class II values (Fig. 5c). Therefore, the characteristics of δ13CPOM and δ15NPOM in classes I, II, III, and IV were middle-δ13CPOM and low-δ15NPOM, middle-δ13CPOM and high-δ15NPOM, low-δ13CPOM and high-δ15NPOM, and high-δ13CPOM and high-δ15NPOM, respectively.
Environmental conditions (temperature, salinity, nitrate concentration, chlorophyll-a concentration, and C:N ratio) differed significantly among the classes (ANOVA, p < 0.01; Fig. 6). The temperature was lower in class I (mean ± SD: 10.1 ± 2.0°C) and III (12.7 ± 6.0°C) than in classes II (20.1 ± 4.9°C) and IV (24.0 ± 2.6°C) (Fig. 6a). Significant differences in temperature were discerned (p < 0.003, pairwise test with Tukey–Kramer adjacent) among different classes, except between classes I and III (p = 0.44). The only significant difference in salinity among the classes was discerned between classes II (33.76 ± 0.79) and III (34.27 ± 0.18) (p = 0.01, pairwise test with Tukey–Kramer adjacent) (Fig. 6b). The mean salinity with SD of classes I and IV were 33.99 ± 0.06 and 33.91 ± 0.36, respectively (Fig. 6b). The mean nitrate concentration values were high in classes I (4.20 ± 1.31 µM) and III (3.88 ± 3.86 µM), lower in class II (0.51 ± 1.31 µM), and the lowest in class IV (0.05 ± 0.01 µM) (Fig. 6c). The chlorophyll-a concentrations were significantly different among the classes (ANOVA, degrees of freedom [DF] = 3, F-value = 2.643, p = 0.048). However, no significant differences were identified among the pairs (p > 0.058). In particular, the chlorophyll-a concentration was the highest in class IV (0.69 ± 1.33 µg L-1), followed by class I (0.61 ± 0.23 µg L-1), class III (0.58 ± 0.37 µg L-1), and the lowest in class II (0.44 ± 0.48 µg L-1) (Fig. 6d]. Moreover, a high C:N ratio was identified in class IV (7.06 ± 1.50 mol mol-1), which was significantly higher (p < 0.01, pairwise test with Tukey–Kramer adjacent) than in classes II (6.13 ± 1.39 mol mol-1) and III (5.12 ± 1.22 mol mol-1). However, the C:N ratio in class IV did not differ from that in class I (6.05 ± 0.58 mol mol-1) (Fig. 6e).
The T–S diagram demonstrated that the samples of class I were mostly (10 of 11 samples) in the water with the temperature and salinity within the 9.4–11.4°C and 33.877–34.038 ranges, respectively (Fig. 7a). Only a single sample of class I was identified at a temperature and salinity of 4.3°C and 34.037, respectively, in April (Fig. 7a). The one sample classified as class II was identified in April in the surface layer, with the temperature and salinity ranging from 9.4–11.4°C and < 34.038, respectively. The nitrate concentration, δ13CPOM, and δ15NPOM of this sample were 0.28 µM, –25.3‰, and 3.5‰, respectively. However, in the N-σt diagram, the samples were not characterized according to the classes (Fig. 7b).
3.4 Relationships with environmental conditions
According to the GMM, both δ13CPOM, and δ15NPOM exhibited multi-modal distribution. The linear regression was based on the assumption that the dependent variable has a normal distribution; therefore, in this study, the linear regressions were inappropriate for evaluating the relationships. We have provided the results of the linear regression analysis in the supporting information.
The least AIC GLM for δ13CPOM was as follows:
δ13CPOM ~ glm[f(class) + f(layer) + Lat + T + S + Chl + Nitrate] (3). The r2 value of the least AIC δ13CPOM model was found to be 0.659. ANOVA indicated that all the remaining explanatory variables were significant (χ2 ≥ 7.53, p ≤ 0.006). The responses of latitude, salinity, and nitrate concentration were significantly negative (p < 0.001), whereas those of temperature and chlorophyll-a concentration were significantly positive (p < 0.001) (Fig. 8). The lsmean δ13CPOM with ANOVA suggested that classes III (lsmean ± SE: –25.6 ± 0.25‰) and IV (–20.6 ± 0.19‰) were significantly higher and lower than those of other classes, respectively (pair-wise test with Tukey’s adjacent, p < 0.001). The difference between classes I (–23.0 ± 0.36‰) and II (–23.4 ± 0.10‰) was insignificant (pair-wise test with Tukey’s adjacent, p = 0.542) (Fig. 8a). The δ13CPOM of the surface (lsmean ± SE: –23.4 ± 0.11‰) was higher than that of the subsurface (–23.7 ± 0.13‰, pair-wise test with Tukey’s adjacent, p = 0.0154). Moreover, δ13CPOM at the 100-m depth (–22.4 ± 0.34‰) was significantly higher than those of the surface and subsurface (p ≤ 0.001) (Fig. 8).
The least-AIC δ15NPOM GLM was as follows:
δ15NPOM ~ glm[f(class) + f(season) + f(layer) + Lon + Lat + T + S] (4). All the parameters were statistically significant (ANOVA, p < 0.001), and the r2 value of the model was found to be 0.451. The longitude had a positive impact on δ15NPOM, i.e., δ15NPOM increased eastward (Fig. 9e). The δ15NPOM was negatively affected by the latitude, temperature, and salinity (Fig. 9d, f, g). The lsmean values indicated that the δ15NPOM of classes I (lsmean ± SE: –1.6 ± 0.41‰) and II (–3.1 ± 0.22‰) were significantly lower and higher than those of the other three classes, respectively (pair-wise test with Tukey’s adjacent, p < 0.01). Moreover, classes III (2.1 ± 0.34‰) and IV (2.0 ± 0.29‰) were not significantly different (p = 0.9735). The seasonal variations exhibited significant differences between spring (1.3 ± 0.25‰) and summer (1.9 ± 0.18‰, pair-wise test with Tukey’s adjacent, p < 0.01), while the lowest lsmean values were recorded in winter (0.9 ± 0.43‰). No significant differences of lsmean δ15NPOM were found between spring and summer (p > 0.0897). The layer indicated that δ15NPOM at a depth of 100 m (2.3 ± 0.39‰) was significantly higher (pair-wise test with Tukey’s adjacent, p < 0.01) than that at the surface (0.9 ± 0.15‰) and subsurface (1.0 ± 0.21‰).
4. Discussion
This study, for the first time, revealed δ13CPOM and δ15NPOM in a wide area of the southern SOJ. Previous studies on δ13CPOM and δ15NPOM were confined to the coastal areas of the southern SOJ (Antonio et al., 2012; Nakamura et al., 2022) and sinking particles (Nakanishi and Minagawa, 2003). Seasonality was demonstrated by the δ13C and δ15N values of sinking organic particles in the deep layer (> 500 m depth) (Nakanishi and Minagawa, 2003). Significant seasonality of δ13CPOM and δ15NPOM was observed on the surface in our study, although the pattern of δ13C differed from that seen by Nakanishi and Minagawa (2003). In Nakanishi and Minagawa (2003), the δ13C of sinking particles increased with the bloom period. However, in our study, the δ13CPOM decreased along the SI-line. On the contrary, the seasonality of δ15N of the sinking particles and δ15NPOM was comparable. We were unable to determine why the seasonality of δ13CPOM of our study differed from that of Nakanishi and Minagawa (2003). However, our observations indicated that the δ13CPOM and δ15NPOM values at 100-m depth, below the euphotic layer, were significantly different from those in the euphotic zone (Fig. 2), and monthly variations were not significant below the mixed layer. Thus, the characteristics of the sinking particles in the SOJ may be different from those of POM in the surface layer.
4.1. Variations and the causes of carbon isotope ratio
According to the GLM approach, environmental characteristics may explain approximately two-thirds of the variations in δ13CPOM. The relationships found in this study are consistent with previous observations. When environmental conditions were ignored, seasonal variation was significant; nevertheless, seasonal variation was not selected as an explanatory variable in the GLM, suggesting that environmental parameters can well explain the seasonality of δ13CPOM. The positive associations observed between temperature or chlorophyll-a concentration and δ13CPOM were similar to those observed in previous ocean and incubation experiments (Fontugne and Duplessy, 1981; Goericke and Fry, 1994; Miller et al., 2013; Savoye et al., 2003). Phytoplankton community growth rates are probably high in the warm water (Sherman et al., 2016), and high chlorophyll-a concentrations are considered to be a consequence of rapid phytoplankton growth. During the rapid growth phase, phytoplankton utilizes more 13CO2 utilization in the water, consequently elevating δ13CPOM (Freeman and Hayes, 1992).
The negative relationship between δ13CPOM and salinity or nitrate concentration has been previously reported in Kuroshio and western North Pacific boundary currents (Kodama et al., 2021). The negative relationship between salinity in the Kuroshio area was considered to be a mixture of POM formed in the estuary (Kodama et al., 2021), where phytoplankton bloom occurs and then high δ13CPOM POM are formed (Savoye et al., 2003; Ogawa and Ogura, 1997). Additionally, the elevated δ13CPOM may be due to sediment resuspension in the Changjiang estuary (Gao et al., 2014). Although our samples were occasionally obtained near Japanese coastal regions, the less saline water of this sea is mostly due to the influence of the ECS (Kosugi et al., 2021). In the ECS, δ13CPOM is negatively associated with salinity (Ho et al., 2021), and high δ13CPOM (> –20‰) is detected in the Changjiang diluted waters (Gao et al., 2014). However, Gao et al. (2014) did not report the hydrographic characteristics in the high δ13CPOM area, and thus, the relationship between salinity and δ13CPOM was unknown. The direct influence of terrestrial organic matter from China may be ignored: the δ13CPOM in the Changjiang river is < –25‰ (Gao et al., 2014), while the contribution of terrestrial organic matter is <10% 500 km away from the Changjiang estuary (Wu et al., 2003). The effects of Japanese local rivers cannot be ignored, where river-originated δ13CPOM is ~ –24‰ and lower than the ocean-originated δ13CPOM (Antonio et al., 2012), and thus, the direct influence of terrestrial organic matter is deemed to be restricted in the SOJ based on the relationship between salinity and δ13CPOM. These results suggest that POM with high-δ13CPOM from the less saline waters of the ECS is transported into the SOJ and influences the spatiotemporal variation of δ13CPOM in the SOJ, particularly during the summer, when the Changjiang-origin freshwater inputs to the SOJ via the Tsushima Strati are the highest among the seasons (Morimoto et al., 2009). However, in the instance of the Delaware Estuary in the United States, high seasonality of δ13CPOM (–17‰ in spring while –32‰ in summer) was reported (Fogel et al., 1992), and it was a possibility that POM with low-δ13CPOM was formed in both the Changjiang estuary and Delaware Estuary. Carbon assimilation and decomposition processes in the less-saline waters, which were not observed in this study, must thus be investigated in the future. We could not come up with a plausible explanation for the negative relationship between latitude and nitrate concentration.
The environmental parameters properly explained δ13CPOM variability, but not the difference among classes (III and IV) because it was significant in the GLM. Temperature, nitrate concentration, and C:N ratio were found to be significantly different across classes III and IV. Furthermore, the class IV samples were mostly from the surface layer during the summer, but the surface layer samples during summer were not classified as the class III. Nakatsuka et al. (1992) reported that δ13CPOM levels are high in the late phytoplankton bloom phase. Thus, the high δ13CPOM with low nitrate concentration and high C:N ratio in the class IV samples may be attributed to active carbon assimilation under nitrate depletion conditions. However, because the chlorophyll a concentration was low in the class IV samples, additional processes must be considered. According to the T-S diagram, class IV POM was mainly collected in the warm and saline waters in 2019. This may indicate that the isotope fraction in this water is different; for example, an increase in diatom abundance elevates δ13CPOM (Lowe et al., 2014), but the diatom contribution in the SOJ is low during the summer (Kodama et al., 2022b). The phytoplankton community structure was not assessed in this study and will need to be investigated more in the future. In contrast to class IV samples, low δ13CPOM samples in class III were mostly observed in nitrate-rich waters, indicating that primary production is not active, which might be due to light limitation and deep mixing; the previous study’s iron limitation for primary production was rejected (Fujita et al., 2010). However, comparable environmental conditions of classes III and IV samples were observed in the classes I and II samples, and hence the fundamental reason why the δ13CPOM was low and high in classes III and IV, respectively, is yet unclear.
4.2. Unique nitrogen dynamics in the SOJ
Unlike δ13CPOM, δ15NPOM was not adequately explained by environmental parameters based on the detection coefficient values. Temperature and salinity remained as explanatory variables in the variation of δ15NPOM in the ocean, but were not regarded as essential determinants (Sigman et al., 2009). Temperature had the opposite impact reported in the Kuroshio (Kodama et al., 2021). Temperature had no significant effect in the western south ECS (Ho et al., 2021), thereby suggesting that the negative impact of temperature on the δ15NPOM was specific to the SOJ; however, its mechanisms remain unclear.
The negative impact of salinity was similar to that observed in the Kuroshio (Kodama et al., 2021). In June 2010, the δ15NPOM in the surface less-saline Changjiang diluted water (at 5 m depth and salinity <30) was recorded to be ~9‰ in the northern part of the ECS (Sukigara et al., 2017). In 2011, the δ15NPOM in the Changjiang diluted water in 2011 was ~6‰ (Sukigara et al., 2017). Sukigara et al. (2017) stated that POM with high δ15NPOM was not always present in the Changjiang diluted water, although high δ15NPOM was also reported in the ECS in previous studies (Gao et al., 2014; Wu et al., 2003). These findings corroborate the hypothesis that high δ15NPOM levels originate from the less-saline Changjiang diluted water that mainly flows into the SOJ during the summer (Morimoto et al., 2009), as well as the negative relationship between salinity and δ15NPOM.
There are two proposed mechanisms for the influence of latitude on the δ15NPOM. One is the Japanese territorial influence. The other is the impact of the coastal branch of the Tsushima Warm Current. The coastal branch of the Tsushima Warm Current originates from the eastern channel of the Tsushima Strait and flows along the Japanese coast (Katoh, 1994). Our monitoring regions always included a coastal region in the low latitude area, as well as flows of the coastal branch of the Tsushima Warm Current (Katoh, 1994). The δ13CPOM did not indicate direct territorial inputs in this case, thus the influence of the coastal branch of the Tsushima Warm Current and the origin of the waters may be the explanation.
Although ocean observations indicated a negative relationship between δ15NPOM and nitrate concentration based on Rayleigh fractionation (Sigman et al., 2009) has been supported in the ocean observations (Kodama et al., 2021; Ho et al., 2021), the negative relationship in the SOJ remained equivocal. Even in an open system, kinetic isotope effects (δ15N difference between reactant and product) and the degree of consumption of reactant theoretically determine the δ15N value of the product, and when the remaining reactant is zero (i.e., completely consumed), the δ15N of a product is equal to the original δ15N of reactant (Sigman et al., 2009). In this case, the kinetic isotope effect of nitrate on POM is ~3‰ (Sigman et al., 2009). Thus, δ15NPOM is theoretically 0–3‰ lower than δ15N of nitrate (δ15NNO3), and with nitrate consumption, it increases and approaches the original δ15NNO3 value. The monthly variations in the surface layer along the SI-line indicated that the δ15NPOM increases from winter to summer, and the GLM method confirms this trend. This also indicates that nitrate depletion partly contributes to the increase in δ15NPOM. Because δ15NPOM was not normally distributed, the association was insignificant (p = 0.4467) when we removed class I POM, indicating that the relationships between δ15NPOM and the environment, especially the nitrate concentration in the SOJ, were unique.
The following are three hypotheses for the ambiguous relationship between δ15NPOM and nitrate concentration: (1) our dataset; and (2) the variety of nitrogen sources. First, our observations were mainly conducted in the summer, during which time the nitrate was depleted at the surface, and the nitrate concentration in 182 of the 494 samples was not detectable (< 0.01 µM). The δ15NPOM in the nitrate-depleted waters varied greatly (mean ± SD: 2.8 ± 1.2‰, n = 182). This δ15NPOM variation in nitrate-depleted water may have rendered the relationship between δ15NPOM and nitrate concentration unclear, rendering the association statistically insignificant. When the GLM approach was conducted for subsamples with detectable nitrate (>0.01 µM), the nitrate concentration remained as the explanatory variable in the least-AIC model; the coefficient was negative but not significant (ANOVA, p = 0.12). As a result, the imbalanced dataset was not rejected; nonetheless, it was not the primary cause of the ambiguous relationship between δ15NPOM and nitrate concentration.
Second, the nitrogenous (nitrate) source of the SOJ exhibited variability. Previous studies in the Northern ECS (Umezawa et al., 2014; Umezawa et al., 2021) found four nitrate sources with varying δ15NNO3. The nitrate with high-δ15NNO3 (8.3‰) originated from the Changjiang freshwater in July, the nitrate with low-δ15NNO3 (2.0‰) originated from the Changjiang estuary in July, the δ15NNO3 in the water originating from the Kuroshio is 5.5–6.0‰ in February, and that originating from atmospheric deposition is -4‒0‰ (Umezawa et al., 2014; Umezawa et al., 2021). Furthermore, active nitrogen fixation occurs in the northwestern part of the ECS (Shiozaki et al., 2010), and δ15NPOM originating from nitrogen fixation is -2.1‒0.8‰ (Minagawa and Wada, 1986). According to these results, the δ15NPOM formed by nitrate assimilation and nitrogen fixation exhibited a wider range. In fact, in the ECS, where the TWC originates, δ15NPOM near the surface varies widely by about -5–9‰ during summer (Gao et al., 2014) and 2–6‰ in autumn (Wu et al., 2003). Horizontal advective transport of nitrate from the ECS is the key factor for controlling primary production in the SOJ throughout the summer (Kodama et al., 2015; Kodama et al., 2017). POM originating from diverse nitrogenous sources will be mixed during the horizontal advection processes in the TWC and the ECS, and POM from the ECS is expected to flow into the SOJ with nitrate. The numerous nitrogen source contributions would obscure the δ15NPOM and nitrate concentration in the SOJ.
Here, a simulation of the relationship between the δ15NPOM and nitrate concentration was performed. The δ15NNO3 was set to 0–8.3‰ (Umezawa et al., 2014; Umezawa et al., 2021), the kinetic isotope effects to 3‰ (Sigman et al., 2009), and the supplied nitrate concentration to 0.05–5 µM. The δ15NPOM was then calculated by mixing nitrate-origin POM and nitrogen-fixation-origin POM. Based on observations in the southern ECS in summer, the contribution of nitrogen fixation to nitrate assimilation in the water column was reported as 10–82% (Liu et al., 2013).The contribution of nitrogen fixation to the primary production in Liu et al. (2013) (1.9–5.8%) was comparable to that of the SOJ in June (~3.8%) (Sato et al., 2021). Therefore, the contribution of nitrogen fixation to δ15NPOM was set at 10–82% (Liu et al., 2013). δ15NPOM produced with nitrogen fixation was set at -2.1–0.8‰ (Minagawa and Wada, 1986). Assuming that the parameters and fraction of remanent nitrate (0–100%), except kinetic isotope effects, were randomly varied in this simulation, and the sample size was set to 500, the insignificant relationship between the δ15NPOM and nitrate concentration was observed in ~30% of the simulations (replicated 1000 times). On the other hand, when the δ15NNO3 was adjusted to 5–6‰, the significant negative relationship between δ15NPOM and nitrate concentration was consistently observed. This result supports our hypothesis that the relationship between the δ15NPOM and nitrate concentration is disrupted by the numerous nitrogen sources.
Another distinctive feature of the SOJ was the low-δ15NPOM designated class I, which was found in the winter and spring, and is characterized in the T-S diagrams. The low-δ15NPOM was mainly observed in temperature and salinity ranges of 9.4–11.4°C and 33.877–34.038, respectively. Wagawa et al. (2020) classified this water as "upper low salinity water" (ULSW). Despite the fact that the origin of USLW was unclear, Wagawa et al. (2020) proposed that it originated from Toyama Bay, with a less saline condition caused by the mixing with local Japanese rivers. It is reasonable to assume that the USLW is not mixed with the saline TWC water (its salinity is ~ 34.5). Because this saline TWC water originates from the Kuroshio Current, the δ15NNO3 of the saline TWC water is estimated to be 5.5–6.0‰ in accordance with Umezawa et al. (2014). At the same time, the δ15NNO3 of local Japanese rivers has been estimated to be 0–2‰ (Sugimoto et al., 2019), suggesting that the POM in the ULSW may have originated from lower δ15NNO3-nitrate than the Kuroshio-origin nitrate, and hence the δ15NPOM is lower than the other water masses. The horizontal distribution of ULSW is not reported, but we assumed that it was not large and confined to winter and spring based on Wagawa et al. (2020), hence the low-δ15NPOM area would be limited to season and area. Phytoplankton bloom is another possibility for class I. δ15NPOM rapidly declined at the start of the phytoplankton bloom phase but quickly rose with nitrate depletion (Nakatsuka et al., 1992). However, we believe that such scenario is uncommon and did not have a significant impact on our observations because the majority of class I samples were collected in February, and the phytoplankton bloom occurred at the end of March (Kodama et al., 2018; Maúre et al., 2017).
Seasonality remained significant after accounting for hydrographic conditions in the GLM. Not only nitrate concentrations but also nitrogen sources have a seasonality in the SOJ. The mixed layer deepens in the winter, and hence the nitrate originated in deep-sea water or local Japanese rivers in this season. The utilization of nitrate provided in winter occurs in spring, and the Tsushima Warm Current remains weak (Yabe et al., 2021). Kodama et al. (2015) also showed that subsurface nutrient maximum induced by the horizontal advective transport of the Tsushima Warm Current could be observed from the beginning of June. Therefore, the seasonality of δ15NPOM may be associated with horizontal advective transport, albeit additional research is required to understand the seasonality.
We identified the interannual variations in δ13CPOM and δ15NPOM during summer using repetitive measurements along the SI-line. In 2019, low δ15NPOM with high δ13CPOM was observed compared to that in 2018, despite the fact that the environmental conditions were not significantly different among these four years (2016, 2018–2020). These interannual variations may be influenced by horizontal advection. We were unable to gather data on the contribution of the horizontal advection processes, however, interannual variations in salinity and phosphate concentration in the ECS are linked to those of SOJ (Kodama et al., 2016; Kosugi et al., 2021). In the ECS, the interannual difference in δ15NPOM in the Changjiang diluted water was reported by Sukigara et al. (2017) and Ho et al. (2021) revealed an interannual difference in δ15NPOM in the Changjiang diluted water in the ECS, which may influence δ13CPOM and δ15NPOM in the SOJ. The causes of interannual variations are not described by Sukigara et al. (2017) or Ho et al. (2021), and we were unable to determine them in our study.
5. Conclusions
In this study, the δ13CPOM and δ15NPOM values in the southern SOJ were investigated and reported for the first time. Our observations were mostly conducted in the summer, therefore our identified characteristics of δ13CPOM and δ15NPOM mainly reflected the characteristics of the summer of SOJ, while seasonality, except autumn, was covered by the monitoring line. There were significant seasonal variations in δ13CPOM and δ15NPOM in the surface mixed layer, but not below the mixed layer (at 30 m depth). Environmental variables and primary production processes adequately explained the observed δ13CPOM value. δ13CPOM could be estimated using our GLM and routine hydrographic observations. However, environmental variables did not adequately explain the variation in δ15NPOM. In particular, the relationship between nitrate concentration was not found. The SJO contains different nitrogenous sources, such as atmospheric depositions and riverine inputs, and δ15NPOM indicates that these sources are mixed and support primary production. The simulation supported that multiple nitrate sources contributed to the ambiguous relationship between δ15NPOM and nitrate concentration. The main nitrogen source in the SOJ was not detected in our study, but the new production was dependent on the nitrate supplied from these sources. Anthropogenic nitrogen inputs were increased in the SOJ (Duan et al., 2007; Kitayama et al., 2012), and hence, the new production in the SOJ is expected to increase. As a result, we must evaluate the impact of “increased” new production on the biogeochemical cycles and ecosystems in the SOJ.
Code and Data availability
Author contribution
TK and KN designed the experiments. All carried them out. TK and AN prepared the manuscript with contributions from all co-authors.
Competing interests:
The authors declare that they have no conflict of interest.
Acknowledgements
We would like to thank the captains, crews, researchers, and staff who took part in the cruises. In particular, we would like to thank Keiko Yamada for her assistance with the stable isotope analysis. We state that there is no conflict of interest, and the Japanese government granted authorization for all of observations in accordance with the Japanese legislation. This work has been funded by a research and assessment program for fisheries resources from the Fisheries Agency of Japan, grants from the Japan Fisheries Research and Education Agency, and Japan Society for the Promotion of Science grants for Taketoshi Kodama, Yosuke Igeta, and Yoichi Kogure (19K06198).
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