**5. References**

**4**

*France*

Laurent Lamaignère

**Laser-Induced Damage Density**

*CEA, DAM, CESTA, BP N°2, F-33114 Le Barp,*

**Measurements of Optical Materials**

The prediction of the lifetime of optics in high power fusion lasers is a key point for mastering the facilities (Bercegol et al., 2008). The laser damage scenario is seen as occurring in two distinct steps. The first one concerns the damage occurrence due to the first optic irradiation: the initiation step (Feit et al., 1999). Afterwards, damage sites are likely to grow with successive new shots: the growth step (Norton et al., 2006 ; Negres et al., 2010). The damage growth study requires the use of large beams due to the exponential nature of the process, leading to centimetre damage sites. At the same time, damage density measurements on large optics are mainly performed off-line by raster scanning the whole component with small Gaussian beams (~1mm) (Lamaignère et al., 2007), except for a wide range of results using larger beams which permit also a comparison between procedures (DeMange et al., 2004). Tests are currently done at a given control fluence. The goal is to irradiate a known area in order to reveal all the defects which could create damage. This procedure, named *rasterscan procedure*, gives access to the optical damage densities (Lamaignère et al., 2007). This measurement is accurate and reproducible (Lamaignère et al., 2010). In standard tests (ISO standards, 2011), results are given in terms of damage probability. This data treatment is size dependent; on the contrary, it is important to focus

The purpose of the present chapter is to explain how the knowledge of the entire test parameters leads to a comparable and reproducible metrology whatever beam and test characteristics. To this end, a specific mathematical treatment is implemented which takes into account beam shape and overlap. This procedure, which leads to very low damage densities with a good accuracy, is then compared with 1/1 tests using small beams. It is also presented with a peculiar attention on data reduction. Indeed an appropriate treatment of these tests results into damage densities gives at once good complementarity of the several procedures and permits the use of one procedure or the other, depending on the need. This procedure is also compared with tests realized with large beams (centimetre sized as used on high power facilities). That permits to compare results given by specific table-top test

In section 2, facilities using small and large beams are described. The several procedures used are presented in section 3. Developments of data treatment and analysis are given in section 4. Section 5 is devoted to the determination of error bars on both the fluence

the attention rather on the density of sites that damage at a given fluence.

facilities with those really obtained on laser lines (Lamaignère et al., 2011).

**1. Introduction**


*1.3. Novelty and Objectives*

Wave energy is approached in several disciplines with a focus on different project stages, creating clusters of information that are interconnected, yet seldom integrated into the literature. This study applies concepts of interdisciplinary research into wave energy in the context of Pacific Island countries, combining a diverse range of elements from the PESTEL dimensions into a framework. Inserting wave energy into a new location will not only have technological and economic repercussions but will also affect the local environment and society. An integrative framework enables us to represent the diversity of issues and delineate the important variables that can turn a project unfeasible or create fundamental dissents between decision makers and stakeholders.

The options for the economic development of the PICs are restricted by limited natural resources, remoteness, and small land size. Local communities rely on sustainable use of their local resources for subsistence and income, which makes coastal and marine resources paramount to the local economy, society, and culture. Marine energy development is highly susceptible to local acceptance in this region, thus, identifying conflicts of use through a general framework will be crucial. There are four main objectives behind this study:

The outcomes include a cluster diagram that represents the ICFPICs, a decision tree for the process of utilizing the framework, and a SWOT diagram constructed based on the information gathered. Section 2 describes the materials and methods for this study, including the resources used to construct the ICFPICs and the methodologies applied. Section 3 presents the results, which incorporates the ICFPICs diagram, a user-friendly decision tree, and a combined SWOT-PESTEL matrix. Discussions and case studies for

Pacific Island countries can be found in Section 4, while conclusions are summarized in Section 5.


**1. Introduction**

Gastroesophageal reflux disease (GERD), commonly known as heartburn, has been one of the most prevalent digestive disorders for the past few decades. Despite various definitions in different parts of the world, GERD generally refers to the effortless movement of stomach contents into the esophagus causing troublesome symptoms, typically a burning sensation in the chest, which may radiate toward the neck, throat, and the back, inducing pain. Chronic GERD can lead to several complications, including erosive esophagitis, esophageal strictures, and esophageal epithelial transformation into Barrett's Esophagus (squamous epithelium turning into columnar epithelium), a precancerous condition to adenocarcinoma. Therefore, early diagnosis and proper treatment are critical for the prevention of these potential complications and malignancy. Due to its popularity, GERD has not only deteriorated the quality of life for many people all over the world, but it has also brought up tremendous economic pressure on many countries and regions. European Digestive Health Summit 2018 reported a 26% reduction in productivity across Europe because of GERD, costing employers ~\$4.4 billion [1]. In the United States, the expenses on GERD were estimated to be at least \$24 billion/year [2].


**Abstract**

Austenitic stainless steel type AISI304 sheets and plates as well as finegrained type AISI316 (FGSS316) substrates and wires were employed as a work material in the intense rolling, the piercing and the plasma nitriding. AISI304 sheet after intense rolling had textured microstructure in the rolling direction. Crystallographic state changed itself to have distorted polycrystalline state along the shearing plane by piercing, with the strain induced phase transformation. FGSS316 substrates were plasma nitrided at 623 K for 14.4 ks to have two-phase fine nanostructure with the average grain size of 100 nm as a surface layer with the thickness of 30 μm. FGSS316 wires were also plasma nitrided at the same conditions to form the nitrided surface down to the depth of 30 μm. This nitrided wire was further uniaxially loaded in tensile to attain more homogeneously nitrided surface nano-structure and to form the austenitic and martensitic fiber structure aligned in the tensile direction. Each crystallographic structure intrinsic to metals and metallic alloys was tailored to have preferable micro−/nano-structured cells by metal forming and nitrogen supersaturation. The crystallographic change by metal forming in a priori and posterior to nitriding was discussed to find out a new way for materials design.

**Keywords:** micro-/nano-texturing, stainless steels, rolling, piercing, plasma nitriding, a priori metal forming to nitriding, posterior nitriding to metal forming


Effect of Sodium Acetate and Trace Element (Se2+, Zn2+) on Exopolysaccharide Production by *Lactobacillus plantarum* and Promote Antioxidant Capacity

*Kusmiati Kusmiati, Yeni Yuliani, I. Nyoman Sumerta, Diah Radini Noerdjito, Wahidin Wahidin, Ghina Puspita Anggraeni, Yosephin Yosephin, Agung Tri Laksono and Atit Kanti*


**Author details**

Nnaemeka Uwaezuoke Department of Petroleum Engineering, School of Engineering and Engineering Technology, Federal University of Technology Owerri (FUTO), Owerri, Nigeria

\*Address all correspondence to<EMAIL_ADDRESS>
© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Perspective Chapter: Rheological Considerations for Drilling and Enhanced Oil Recovery… DOI: http://dx.doi.org/10.5772/intechopen.106897*

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Section 2


**5.3. Imaging techniques**

Radiological investigations confirm the suspicion of neonatal osteomyelitis, define the infection site, differentiate between unifocal and multifocal disease patterns and identify secondary complications. Computed tomography, magnetic resonance imaging, ultrasound, radiography and bone scintigraphy scanning have been reported to be useful in detecting osteomyelitis. However, awareness of radiation exposure, need for sedation and transfer to another unit must be considered in the selection of technique.

Radiographs should be the first diagnostic assessment to be performed in patients with suspected osteomyelitis, because they may suggest the correct diagnosis and exclude other pathologic conditions (Figure 2a). However, the specificity of plain radiographs for detecting osteomyelitis is greater (75% to 83%) than its sensitivity (43% to 75%) [33]. Plain radiography can show soft tissue swelling and destroyed fascial planes within days after onset of infection, but may be subtle and not obvious until day 5 to 7 in children [34]. In the neonate even soft tissue swelling may not be present, because subcutaneous fat is lacking and fascial planes are poorly defined. Joint effusions might be suspected if widening of the joint space or bulging of the soft tissues is detected. Additional early changes are as follows: periosteal thickening/elevation, lytic lesions, osteopenia, loss of trabecular architecture, and new bone apposition [35]. Of importance, destructive bone changes do not appear until 7 to 14 days of disease [25].

Predominately in children, ultrasound can detect features of acute osteomyelitis several days earlier, than radiographs [34]. Even though findings may not be specific and standardized reports for neonates with osteomyelitis are lacking, ultrasound should be taken into account as a useful additional diagnostic tool for the early detection and management of osteomyelitis in neonates as it has many advantages: it is non-invasive, readily accessible, performed bedside, of minimal discomfort for the patient, does not use ionizing radiation and does not need sedation [36,37]. Even though ultrasound cannot exclude the diagnosis of osteomyelitis, its main value lies in its ability to identify involvement of the adjacent soft tissue (subperiosteal fluid collection or abscess formation), periosteal thickening or elevation, joint effusions and irregularities or interruptions of the cortical bone (Figure 2b) [38,39]. Color Doppler imaging further supports the diagnostic assessment, showing coexisting presence of hyperemia surrounding the periost and soft tissue abscess formation. Ultrasound can also be used to image guided-needle aspiration of the subperiosteal fluid for pathogenic organism isolation or subperiosteal abscess drainage. Furthermore, ultrasound has been described as being helpful in differentiating between epiphyseal separation and subluxation following septic arthritis [40]. However, ultrasound cannot exclude the diagnosis of acute osteomyelitis, and thus further imaging diagnostics may be required [41,42].

36 Neonatal Bacterial Infection

**5.3. Imaging techniques**

14 days of disease [25].

days. Thus, normal values do not preclude the diagnosis [30]. The C-reactive protein (CRP) is a rapid indicator of systemic inflammation and tissue damage, is useful as acute phase reactant, but not specific for skeletal infection. Procalcitonin has also been described as a potential marker in the diagnosis of osteomyelitis in children, but needs to be investigated in larger trials, especially in newborns [31,32]. Elevated values of CRP and erythrocyte sedimentation rates could be used to monitor response to therapy or identify complications.

Radiological investigations confirm the suspicion of neonatal osteomyelitis, define the infection site, differentiate between unifocal and multifocal disease patterns and identify secondary complications. Computed tomography, magnetic resonance imaging, ultrasound, radiography and bone scintigraphy scanning have been reported to be useful in detecting osteomyelitis. However, awareness of radiation exposure, need for sedation and transfer to

Radiographs should be the first diagnostic assessment to be performed in patients with suspected osteomyelitis, because they may suggest the correct diagnosis and exclude other pathologic conditions (Figure 2a). However, the specificity of plain radiographs for detecting osteomyelitis is greater (75% to 83%) than its sensitivity (43% to 75%) [33]. Plain radiography can show soft tissue swelling and destroyed fascial planes within days after onset of infection, but may be subtle and not obvious until day 5 to 7 in children [34]. In the neonate even soft tissue swelling may not be present, because subcutaneous fat is lacking and fascial planes are poorly defined. Joint effusions might be suspected if widening of the joint space or bulging of the soft tissues is detected. Additional early changes are as follows: periosteal thickening/elevation, lytic lesions, osteopenia, loss of trabecular architecture, and new bone apposition [35]. Of importance, destructive bone changes do not appear until 7 to

Predominately in children, ultrasound can detect features of acute osteomyelitis several days earlier, than radiographs [34]. Even though findings may not be specific and standardized reports for neonates with osteomyelitis are lacking, ultrasound should be taken into account as a useful additional diagnostic tool for the early detection and management of osteomyelitis in neonates as it has many advantages: it is non-invasive, readily accessible, performed bedside, of minimal discomfort for the patient, does not use ionizing radiation and does not need sedation [36,37]. Even though ultrasound cannot exclude the diagnosis of osteomyelitis, its main value lies in its ability to identify involvement of the adjacent soft tissue (subperiosteal fluid collection or abscess formation), periosteal thickening or elevation, joint effusions and irregularities or interruptions of the cortical bone (Figure 2b) [38,39]. Color Doppler imaging further supports the diagnostic assessment, showing coexisting presence of hyperemia surrounding the periost and soft tissue abscess formation. Ultrasound can also be used to image guided-needle aspiration of the subperiosteal fluid for pathogenic organism isolation or subperiosteal abscess drainage. Furthermore, ultrasound has been described as being helpful in differentiating between

another unit must be considered in the selection of technique.

(a)

**Figure 2.** Acute osteomyelitis of the right humerus. **a)** periosteal elevation and soft tissue swelling **b)** joint effusion and synovial thickening

Magnetic resonance imaging (MRI) has high specificity (94%) and sensitivity (97%) for the diagnosis of acute osteomyelitis, showing changes as early as day 3 to 5 after the onset of infection [43,44]. MRI gives excellent tissue characterization and high resolution, showing detailed anatomic presence of the inflammatory process and its complications (abscess formation, physeal involvement, septic arthritis), further allowing the assessment of involvement of the growth plate and epiphysis. MRI has been proven useful in the diagnosis of clinically suspected osteomyelitis in children [45-48], but for its use in neonatology it has several limitations: first and foremost the need for sedation and transfer to the MRI unit.

Three-phase bone imaging, using technetium 99m is very sensitive (90%-95%) for the detection of acute osteomyelitis in the early stages of disease and allows detection within 24 to 48 hours after onset of symptoms [34,49]. Bone scintigraphy is especially useful for detecting multiple foci of infection or if the infection site is poorly localized. Technetium-99

methylene diphosphonate accumulates in areas of increased bone turnover and is for now the preferred agent of choice for radionuclide bone imaging. In neonates bone scintigraphy is the subject of controversy: only a few reports support its use and have shown that sensitivity is much lower, than in older infants because of poor bone mineralization [18,48,50].

Neonatal Osteomyelitis 39

obvious focus, in order to facilitate early diagnosis and prompt initiation of appropriate

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[4] Ho NK, Low YP, See HF. Septic arthritis in the newborn--a 17 years' clinical experience.

[5] Caksen H, Oztürk MK, Uzüm K, Yüksel S, Ustünbaş HB, Per H. Septic arthritis in

[6] Goldmann DA, Durbin WA Jr, Freeman J. Nosocomial infections in a neonatal intensive

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[8] De Boeck H. Osteomyelitis and septic arthritis in children. Acta Orthop Belg.

[9] Krogstad P. Osteomyelitis and septic arthritis. In: Feigin RD, Cherry JD, Demmler GJ, et al., eds. Textbook of Pediatric Infectious Diseases. Fifth edition. Philadelphia: Saunders

[11] Frederiksen B, Christiansen P, Knudsen FU. Acute osteomyelitis and septic arthritis in

[12] Ish-Horowicz MR, McIntyre P, Nade S. Bone and joint infections caused by multiply resistant Staphylococcus aureus in a neonatal intensive care unit. Pediatr Infect Dis J.

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*Article* **First Concurrent Measurement of Primary Production in the Yellow Sea, the South Sea of Korea, and the East/Japan Sea, 2018**

**Hyo-Keun Jang 1 , Seok-Hyun Youn 2 , Huitae Joo 2 , Yejin Kim 1 , Jae-Joong Kang 1 , Dabin Lee 1 , Naeun Jo 1 , Kwanwoo Kim 1 , Myung-Joon Kim 1 , Soohyun Kim 1 and Sang-Heon Lee 1,\***

**Abstract:** Dramatic environmental changes have been recently reported in the Yellow Sea (YS), the South Sea of Korea (SS), and the East/Japan Sea (EJS), but little information on the regional primary productions is currently available. Using the 13C-15N tracer method, we measured primary productions in the YS, the SS, and the EJS for the first time in 2018 to understand the current status of marine ecosystems in the three distinct seas. The mean daily primary productions during the observation period ranged from 25.8 to 607.5 mg C m−2 d −1 in the YS, 68.5 to 487.3 mg C m−2 d −1 in the SS, and 106.4 to 490.5 mg C m−2 d −1 in the EJS, respectively. In comparison with previous studies, significantly lower (*t*-test, *p* < 0.05) spring and summer productions and consequently lower annual primary productions were observed in this study. Based on PCA analysis, we found that small-sized (pico- and nano-) phytoplankton had strongly negative effects on the primary productions. Their ecological roles should be further investigated in the YS, the SS, and the EJS under warming ocean conditions within small phytoplankton-dominated ecosystems.

**Keywords:** primary production; phytoplankton; Yellow Sea; East/Japan Sea; South Sea of Korea

### **1. Introduction**

Marine phytoplankton as primary producers play an important role as the base of the ecological pyramid in the ocean and are responsible for nearly a half of global primary production [1,2]. The primary production of phytoplankton is widely used as an important indicator to predict annual fishery yield in various oceanic regions [3–5], because it is one of key factors in determining amount of food source for upper-trophic-level consumers [6,7]. Lee et al. [8,9] also reported that an algorithm for estimation of the habitat suitability index for the mackerels and squids around the Korean peninsula was largely improved by including a primary production term. The physiological conditions and community structures of phytoplankton are closely related to physical and chemical factors (e.g., light regime, nutrients, and temperature) [10–12], which induce greatly different phytoplankton productions in various marine ecosystems [3,13,14]. Thus, the primary production measurements can provide fundamental backgrounds for better understanding marine ecosystems with different environmental conditions and detecting current potential ecosystem changes.

The Yellow Sea (hereafter YS), the South Sea of Korea (SS), and the East/Japan Sea (EJS), belonging to the East Asian marginal seas, have experienced 2–4 times faster increase (0.7–1.2 ◦C) in seawater temperature than that in global mean water temperature (0.4 ◦C) for 20 years (1983–2006) [15]. Moreover, some notable changes in physicochemical conditions were reported, such as increasing limitation of nutrients in the YS and rapid

**Citation:** Jang, H.-K.; Youn, S.-H.; Joo, H.; Kim, Y.; Kang, J.-J.; Lee, D.; Jo, N.; Kim, K.; Kim, M.-J.; Kim, S.; et al. First Concurrent Measurement of Primary Production in the Yellow Sea, the South Sea of Korea, and the East/Japan Sea, 2018. *J. Mar. Sci. Eng.* **2021**, *9*, 1237. https://doi.org/ 10.3390/jmse9111237

Academic Editor: Antonello Sala

Received: 30 September 2021 Accepted: 5 November 2021 Published: 8 November 2021

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ocean acidification and shoaling of the mixed layer depth in the EJS [16–19]. These recent environmental changes could result in alterations in biological characteristics, including community structure and bloom pattern of phytoplankton and subsequently higher-trophiclevel organisms [12,20–22]. Indeed, biological responses related to phytoplankton were observed in the YS [16,18,23,24] and the EJS [12,22,25,26]. For the YS, a remarkable decrease in chlorophyll-*a* (chl-*a*) concentration and phytoplankton diversity and abundance was observed between the periods 1983–1986 and 1996–1998, which affected the primary production [16]. The phytoplankton community assemblage was also dramatically changed in the YS, especially in the spring [18,23]. For the EJS, the patterns of timing, magnitude, and duration of the spring phytoplankton bloom were significantly different between 1998–2001 and 2008–2011 [25]. Joo et al. [26] found dramatic decreasing trends (1.3% each year) in annual primary productivities in various regions in the EJS for 1 decade (2003–2012) based on the satellite-based data. Nevertheless, we still have a lack of information on regional primary productions of phytoplankton for understanding the current status of the marine ecosystems in the YS, the SS, and the EJS.

In this study, one of our main objectives is to compare the seasonal and regional primary productions measured simultaneously in the YS, the SS, and the EJS for the first time in 2018 with those reported in previous studies in each region. The other one is to determine major controlling factors in the low primary production in the YS, the SS, and the EJS in 2018.

### **2. Materials and Methods**

### *2.1. Water Sampling Collection*

Seasonal cruise surveys were conducted onboard the R/V Tamgu 8 for the YS and the SS and R/V Tamgu 3 for the EJS from February to October 2018 (Figure 1). The data collected from the February, April, August, and October 2018 cruises were designated to represent winter, spring, summer, and autumn, respectively. At mid-morning, 9–10 different stations were determined in the YS and the EJS whereas 6–7 sampling stations were determined for the SS among the monitoring stations (Figure 1) managed by the National Institute of Fisheries Science (NIFS) in Korea (Table 1).

**Table 1.** Description of sampling sites in the YS, the SS, and the EJS for each cruise period, in 2018. (o) means investigation was conducted, while (-) means investigation was not conducted.

**Table 1.** *Cont.*

The bottom depths at our sampling stations in the YS and the SS had relatively narrow range, whereas the EJS had a wide range of bottom depths (48–2340 m) in this study (Table 1). The six water depths were determined at each station by converting Secchi disc depth to 6 corresponding light depths (100, 50, 30, 12, 5, and 1% of surface photosynthetic active radiation; (PAR)). Then, each water sample was collected from 6 different depths using Niskin bottles (8 L) equipped with a conductivity, temperature, and depth (CTD) rosette. The water temperature and salinity were obtained from SBE9/11 CTD (Sea-Bird Electronics, Bellevue, WA, USA). The mixed-layer depth (MLD) was defined as the depth at which the density is increased by 0.125 density units from the sea surface density [27,28]. Water samples for dissolved inorganic nutrients (NH4, NO2 + NO3, PO4, and SiO2) and chl-*a* (total and size-fractionated) concentrations were collected at three light depths (100, 30, and 1% of PAR). Water samples for measuring the particle organic carbon (POC) and particle organic nitrogen (PON) concentrations and total carbon uptake rates (primary production) of phytoplankton were collected at six light depths (100, 50, 30, 12, 5, and 1% of PAR). The euphotic zone is defined as the depth from 100 to 1% of PAR.

**Figure 1.** Locations of sampling regions in 2018. The station numbers are in consecutive order from coast to open sea as marked in each station line. **Figure 1.** Locations of sampling regions in 2018. The station numbers are in consecutive order from coast to open sea as marked in each station line.

#### **Table 1.** Description of sampling sites in the YS, the SS, and the EJS for each cruise period, in 2018. *2.2. Inorganic Nutrients Concentrations*

(o) means investigation was conducted, while (-) means investigation was not conducted. **Region Station Latitude Longitude Bottom Depth (m) Feb. Apr. Aug. Oct.** 307-03 36.92 126.00 37 o o o o 307-05 36.92 125.42 54 o o o o 307-09 36.92 124.57 67 o o o o To measure concentrations of dissolved inorganic nutrients (NH4, NO2 + NO3, PO4, and SiO2), 0.1 L water samples were filtered onto Whatman GF/F filters (ø = 47 mm) at a vacuum pressure lower than 150 mmHg. Filtered water samples were immediately frozen at −20 ◦C for further analysis in our laboratory. An auto-analyzer (Quattro, Seal Analytical, Norderstedt, Germany) in the NIFS was used for the analysis of dissolved inorganic nutrients according to the manufacturer's instruction.

308-06 36.33 125.21 58 - - o o

#### 309-03 35.85 125.82 54 o o o o *2.3.  Water samples (0.1–0.4 L) for total chl-*a* concentration were filtered through Whatman GF/F filters (ø = 25 mm), and samples (0.3–1 L) for three different size-fractionated chl-*a* concentrations were passed sequentially through 20 µm and 2 µm membrane filters (ø = 47 mm) and GF/F filters (ø = 47 mm) at low vacuum pressure. The filtered samples were then placed in a 15 mL conical tube, immediately stored in −20 ◦C freezer until the analysis. In the laboratory, the frozen filters were extracted with 90% acetone at 4 ◦C for 20–24 h, and chl-*a* concentrations were then measured using a fluorometer (Turner Designs, 10-AU, San Jose, CA, USA) calibrated based on commercially available reference material for chl-*a*.

#### 312-09 34.09 124.60 89 o o o o *2.4. Measurements of Phyoplankton Carbon and Nitrogen Uptake Rate*

SS 203-03 33.64 126.36 133 - - - o 204-04 33.90 127.25 75 o o - o 205-03 34.08 127.94 82 o o o o 205-05 33.62 128.15 113 o o o - 206-03 34.37 128.82 92 o o o o 207-03 34.89 129.25 115 o o o o 400-14 34.21 128.40 75 o - o o The 13C-15N dual stable isotope tracer technique was used for simultaneously measuring the carbon and nitrogen uptake rates of the phytoplankton as described by Dugdale and Goering [30] and Hama et al. [31]. In brief, water samples from each light depth (100%, 50%, 30%, 12%, 5%, and 1% of PAR) were immediately transferred to acid-rinsed polycarbonate incubation bottles (1 L) covered with neutral density screens (Lee Filters) [32] after passing through 333 µm sieves to eliminate the large zooplankton. The incubation bottles filled with seawater at each light depth were inoculated with the labeled carbon (NaH13CO3)

and nitrate (K15NO3) or ammonium (15NH4Cl), which correspond to 10–15% of the concentrations in the ambient water [30,31]. Then, the tracer-injected bottles were incubated in a large polycarbonate incubator at a constant temperature maintained by continuously circulating sea surface water under natural surface light for 4–5 h. The incubated water samples (0.1–0.4 L) were filtered onto Whatman GF/F filters (ø = 25 mm) precombusted at 450 ◦C, and the filters were then kept in a freezer (−20 ◦C) until mass spectrometer analysis. At the laboratory of Pusan National University, the filters were fumed with a strong hydro acid in a desiccator to remove the carbonate overnight and dried with a freeze drier for 2 h. Then, POC and PON concentrations and atom % of 13C were analyzed by Finnigan Delta+XL mass spectrometer at the stable isotope laboratory of the University of Alaska (Fairbanks, AK, USA). The carbon uptake rates of the phytoplankton were estimated as described by Dugdale and Goering [30] and Hama et al. [31]. The final values of the carbon uptake rates of phytoplankton were then calculated by subtracting the carbon uptake rates of dark bottles to eliminate the heterotrophic bacterial production [33–35]. The daily primary productions of phytoplankton were calculated from the hourly primary productions observed in this study and 10-h photoperiod per day reported previously in the YS and EJS [22,24].

### *2.5. Statistical Analysis*

The statistical analyses for Pearson's correlation, *t*-test, and one-way analysis of variance (one-way ANOVA) were performed using SPSS (version 12.0, SPSS Inc., Chicago, IL, USA). In the one-way ANOVA, a test to certify the homoscedasticity of variables was conducted by using Levene's test. To compare pairwise differences for the variables, Scheffe's (homogeneity) and Dunnett's (heteroscedasticity) post hoc tests were used, based on homogeneity of variances.

Principal component analysis (PCA) with the Varimax method with Kaiser normalization using the XLSTAT software (Addinsoft, Boston, MA, USA) was used to identify relatively significant factors affecting the total carbon uptake rates of phytoplankton in each sea during our observation time. Fourteen variables for PCA included physical (water temperature and salinity and euphotic and mixed-layer depths), chemical (NH4, NO2+NO3, PO4, and SiO2 concentrations), and biological (total and size-fractionated chl-*a* and POC concentration) factors and carbon uptake rates of phytoplankton.

### **3. Results**

### *3.1. Physicochemical Environmental Conditions*

Seasonal vertical profiles of the mean temperatures and salinities at each light depth in the YS, the SS, and the EJS are presented in Figure 2. Seasonal water temperatures and salinities in the YS, the SS, and the EJS were evenly distributed within the euphotic zone except in August. The mean temperatures within the euphotic zone in the YS, the SS, and the EJS were lowest in February, with means of 5.9 (S.D. = ± 2.3), 13.6 (± 1.3), and 9.9 (± 1.7) ◦C, respectively, and gradually increased to their highest in August, with means of 23.2 (± 1.4), 23.8 (± 1.6), and 20.9 (± 2.9) ◦C, respectively (Figure 2). The average water temperature in the YS was significantly lower than those in the SS and EJS during February and April (one-way ANOVA, *p* < 0.05). The highest mean salinities in the YS and EJS were observed in April (32.8 ± 0.8 and 34.4 ± 0.1 psu), whereas salinity in the SS was highest in February at 34.6 ± 0.0 psu (Figure 2). Overall, lower salinities were found in the YS than in the SS and the EJS throughout the observation period.

**3. Results**

**Figure 2.** Vertical profiles of mean temperatures (**a**) and salinities (**b**) at six light depths (100, 50, 30, 12, 5, and 1%) in the YS, the SS, and the EJS, 2018. **Figure 2.** Vertical profiles of mean temperatures (**a**) and salinities (**b**) at six light depths (100, 50, 30, 12, 5, and 1%) in the YS, the SS, and the EJS, 2018.

Principal component analysis (PCA) with the Varimax method with Kaiser normalization using the XLSTAT software (Addinsoft, Boston, MA, United States) was used to identify relatively significant factors affecting the total carbon uptake rates of phytoplankton in each sea during our observation time. Fourteen variables for PCA included physical (water temperature and salinity and euphotic and mixed-layer depths), chemical (NH4, NO2+NO3, PO4, and SiO2 concentrations), and biological (total and size-fractionated chl-*a*

Seasonal vertical profiles of the mean temperatures and salinities at each light depth in the YS, the SS, and the EJS are presented in Figure 2. Seasonal water temperatures and salinities in the YS, the SS, and the EJS were evenly distributed within the euphotic zone except in August. The mean temperatures within the euphotic zone in the YS, the SS, and the EJS were lowest in February, with means of 5.9 (S.D. = ± 2.3), 13.6 (± 1.3), and 9.9 (± 1.7) ˚C, respectively, and gradually increased to their highest in August, with means of 23.2 (± 1.4), 23.8 (± 1.6), and 20.9 (± 2.9) ˚C, respectively (Figure 2). The average water temperature in the YS was significantly lower than those in the SS and EJS during February and April (one-way ANOVA, *p* < 0.05). The highest mean salinities in the YS and EJS were observed in April (32.8 ± 0.8 and 34.4 ± 0.1 psu), whereas salinity in the SS was highest in February at 34.6 ± 0.0 psu (Figure 2). Overall, lower salinities were found in the YS than in the SS

and POC concentration) factors and carbon uptake rates of phytoplankton.

*3.1. Physicochemical Environmental Conditions*

and the EJS throughout the observation period.

The mean euphotic depths in the YS, the SS, and the EJS were deepest in August at 37.6 ± 15.6, 49.8 ± 11.3, and 54.4 ± 10.7 m, respectively (Figure 3). In particular, the euphotic depth in the EJS in February (51.0 ± 5.8 m) was significantly deeper (one-way ANOVA, *p* < 0.01) than those in the YS (12.8 ± 6.2 m) and the SS (28.1 ± 4.7 m). The deepest MLDs in the YS, the SS, and the EJS were observed in February, with means of 68.7 ± 15.7, 59.0 ± 40.5, and 80.6 ± 57.4 m, respectively (Figure 3). The MLDs in the YS, the SS, and the EJS became continuously shallow until August at 12.0 ± 14.2, 13.7 ± 6.6, and 13.2 ± 6.2 m, respectively, and then deepened in October to 26.3 ± 13.7, 30.2 ± 16.1, and 37.9 ± 14.2 m, respectively. In all regions, the differences between the MLDs and euphotic depths were greatest in February, decreased toward April, and then reversed in August when MLDs were significantly shallower than the euphotic depths (*t*-test, *p* < 0.01) (Figure 3). These results indicate that the euphotic zone was vertically well-mixed in all study regions during February and April, whereas strong stratifications were developed in the euphotic water columns during August.

water columns during August.

**Figure 3.** Variation in the mean euphotic and mixed-layer depths in the YS, the SS, and the EJS, 2018. **Figure 3.** Variation in the mean euphotic and mixed-layer depths in the YS, the SS, and the EJS, 2018.

The mean euphotic depths in the YS, the SS, and the EJS were deepest in August at 37.6 ± 15.6, 49.8 ± 11.3, and 54.4 ± 10.7 m, respectively (Figure 3). In particular, the euphotic depth in the EJS in February (51.0 ± 5.8 m) was significantly deeper (one-way ANOVA, *p* < 0.01) than those in the YS (12.8 ± 6.2 m) and the SS (28.1 ± 4.7 m). The deepest MLDs in the YS, the SS, and the EJS were observed in February, with means of 68.7 ± 15.7, 59.0 ± 40.5, and 80.6 ± 57.4 m, respectively (Figure 3). The MLDs in the YS, the SS, and the EJS became continuously shallow until August at 12.0 ± 14.2, 13.7 ± 6.6, and 13.2 ± 6.2 m, respectively, and then deepened in October to 26.3 ± 13.7, 30.2 ± 16.1, and 37.9 ± 14.2 m, respectively. In all regions, the differences between the MLDs and euphotic depths were greatest in February, decreased toward April, and then reversed in August when MLDs were significantly shallower than the euphotic depths (t-test, *p* < 0.01) (Figure 3). These results indicate that the euphotic zone was vertically well-mixed in all study regions during February and April, whereas strong stratifications were developed in the euphotic

Major dissolved inorganic nutrient concentrations at each light depth (100%, 30%, and 1%) in the YS, the SS, and the EJS for each cruise are summarized in Table 2. The ranges of NO2+NO3, PO4, and SiO2 concentrations during the study period were 0.5–9.9, <0.1–0.6, and 2.4–10.0 μM in the YS; 0.9–8.1, 0.1–0.4, and 5.1–11.3 μM in the SS; and 0.2– 8.7, 0.1–0.5, and 2.4–11.0 μM in the EJS, respectively. Ranges of nutrient concentrations except for NH4 varied significantly in all regions during the study period, being generally high in February and low in other seasons except NO2+NO3 concentrations in the YS in April. The nutrient concentrations, except for NH4 at 1% light depths in the YS and the EJS, were higher (one-way ANOVA, *p* < 0.01) than those at 100% and 30% light depths during August and October, whereas vertical differences in the SS were only detected in August. NH4 concentrations ranged from 0.5 to 1.2 μM in the YS, 0.1 to 0.6 μM in the SS, and 0.4 to 0.9 μM in the EJS, respectively, during the observation period. Unlike other nutrients, NH4 concentrations had no distinct seasonal and vertical characteristics in all Major dissolved inorganic nutrient concentrations at each light depth (100%, 30%, and 1%) in the YS, the SS, and the EJS for each cruise are summarized in Table 2. The ranges of NO2+NO3, PO4, and SiO2 concentrations during the study period were 0.5–9.9, <0.1–0.6, and 2.4–10.0 µM in the YS; 0.9–8.1, 0.1–0.4, and 5.1–11.3 µM in the SS; and 0.2–8.7, 0.1–0.5, and 2.4–11.0 µM in the EJS, respectively. Ranges of nutrient concentrations except for NH4 varied significantly in all regions during the study period, being generally high in February and low in other seasons except NO2+NO3 concentrations in the YS in April. The nutrient concentrations, except for NH4 at 1% light depths in the YS and the EJS, were higher (one-way ANOVA, *p* < 0.01) than those at 100% and 30% light depths during August and October, whereas vertical differences in the SS were only detected in August. NH4 concentrations ranged from 0.5 to 1.2 µM in the YS, 0.1 to 0.6 µM in the SS, and 0.4 to 0.9 µM in the EJS, respectively, during the observation period. Unlike other nutrients, NH4 concentrations had no distinct seasonal and vertical characteristics in all study regions.

study regions. **Table 2.** The dissolved inorganic nutrient concentrations averaged from each light depth (100, 30, and 1%) in the YS, the SS, and the EJS, 2018.

**Table 2.** *Cont.*

### *3.2. Concentrations and Size-Fractionated Compositions of chl-a*

The ranges of the total chl-*a* concentrations integrated throughout the euphotic water column in the YS, the SS, and the EJS were 1.3–96.6, 5.6–60.7, and 8.0–92.9 mg m−2 , respectively, during our observation period (Figure 4). The highest chl-*a* concentration in the YS was detected in April (mean ± S.D. = 31.1 ± 28.6 mg m−2 ), followed by October (18.8 ± 8.0 mg m−2 ), August (12.1 ± 5.0 mg m−2 ), and February (3.3 ± 1.8 mg m−2 ). The chl-*a* concentrations in the SS between April (47.6 ± 10.1 mg m−2 ) and October (44.9 ± 15.3 mg m−2 ) were similar, and the concentrations during these periods were higher (One-way ANOVA, *p* < 0.05) than those in February (11.7 ± 3.9 mg m−2 ) and August (12.8 ± 3.9 mg m−2 ). In the EJS, the highest chl-*a* concentration was observed in April (50.1 ± 12.5 mg m−2 ), and the second-highest concentration was observed in February (38.0 ± 23.9 mg m−2 ). The chl-*a* concentration was lowest in August (12.6 ± 4.6 mg m−2 ).

Based on the size-fractionated chl-*a* concentrations, the compositions of micro- (>20 µm), nano- (2–20 µm), and pico-sized (0.7–2 µm) phytoplankton in the YS, the SS, and the EJS are shown in Figure 5a–c. Overall, the fraction of nano- and pico-sized phytoplankton was dominant in the YS (> 65%), the EJS (> 58%), and the SS (> 65%) during the study period except for October. In detail, the compositions of the nano- and pico-sized phytoplankton in the YS were 50.7 ± 6.1 and 27.0 ± 12.2% in February, 41.6 ± 14.1 and 23.6 ± 10.9% in April, 35.1 ± 7.6 and 45.9 ± 14.7% in August, and 23.4 ± 6.1 and 60.1 ± 21.4% in October, respectively. The compositions of micro-sized phytoplankton in the YS remained low (approximately 19%) during the study period except for April (34.8 ± 22.8%). The contributions of the nano- and pico-sized phytoplankton in the SS during February, April, August, and October were 27.7 ± 4.4 and 63.8 ± 3.5%, 43.3 ± 19.5 and 24.9 ± 13.2%, 36.7 ± 11.5 and 52.4 ± 8.8%, and 15.0 ± 2.6 and 35.6 ± 17.5%, respectively. The highest contribution of micro-sized phytoplankton in the SS was observed in October (49.4 ± 19.5%), followed by April (31.7 ± 30.5%), August (10.9 ± 3.8%), and February (8.4 ± 2.1%). The fractions of nano- and pico-sized phytoplankton in the total chl-*a* concentrations in the EJS in each season were 25.4 ± 9.2 and 42.0 ± 16.6% (February), 28.8 ± 8.3 and 43.8 ± 12.7% (April),

similar, and the concentrations during these periods were higher (One-way ANOVA, *p* <

*J. Mar. Sci. Eng.* **2021**, *9*, x FOR PEER REVIEW 9 of 20

**Figure 4.** Spatial distributions of the chl-*a* concentrations integrated within the euphotic depth from 100 to 1% light depth in the YS, the SS, and the EJS during Feb. (**a**), Apr. (**b**), Aug. (**c**), and Oct. (**d**). **Figure 4.** Spatial distributions of the chl-*a* concentrations integrated within the euphotic depth from 100 to 1% light depth in the YS, the SS, and the EJS during Feb. (**a**), Apr. (**b**), Aug. (**c**), and Oct. (**d**). October (41.5 ± 19.7 %).

**Figure 5.** Seasonal variations in different size compositions of chl-*a* in the YS (**a**), the SS (**b**), and the EJS (**c**), 2018. **Figure 5.** Seasonal variations in different size compositions of chl-*a* in the YS (**a**), the SS (**b**), and the EJS (**c**), 2018.

*3.3. POC and PON Concentration*

1.7 ± 0.5 and 2.7 ± 1.0 g C m−2

6.3 ± 1.2 in the EJS, respectively.

The mean POC concentrations integrated in the euphotic zone in the YS, the SS, and

The POC concentrations had significantly positive correlations (R2 = 0.7575, *p* < 0.01 in the YS; R2 = 0.8105, *p* < 0.01 in the SS; R2 = 0.5723, *p* < 0.01 in the EJS) with the PON concentrations in this study. The average C/N ratios at each month (February, April, August, and October) were 9.2 ± 1.0 (mean ± S.D.), 7.7 ± 0.9, 9.3 ± 0.9, and 18.6 ± 2.3 in the YS; 10.8 ± 2.1, 8.1 ± 1.4, 9.2 ± 1.5, and 11.3 ± 3.0 in the SS; and 8.9 ± 1.2, 6.6 ± 0.8, 12.5 ± 0.9, and

, to October, with 10.4 ± 3.7 and 7.5 ± 3.1 g C m−2, respectively.

ure 6b). The POC concentrations in the YS and SS gradually increased from February, at

In comparison, the POC concentrations in the EJS were the highest during August at 8.9 ± 1.5 g C m−2 but remained constant at an average of ~4 g C m−2 during other seasons.

#### *3.3. POC and PON Concentration* 1.7 ± 0.5 and 2.7 ± 1.0 g C m−2 In comparison, the POC concentrations in the EJS were the highest during August at 8.9 ±

*3.3. POC and PON Concentration*

**Figure 5.** Seasonal variations in different size compositions of chl-*a* in the YS (**a**), the SS (**b**), and the EJS (**c**), 2018.

October (41.5 ± 19.7 %).

The mean POC concentrations integrated in the euphotic zone in the YS, the SS, and the EJS showed different seasonal patterns in comparison to the chl-*a* concentrations (Figure 6b). The POC concentrations in the YS and SS gradually increased from February, at 1.7 ± 0.5 and 2.7 ± 1.0 g C m−2 , to October, with 10.4 ± 3.7 and 7.5 ± 3.1 g C m−2 , respectively. In comparison, the POC concentrations in the EJS were the highest during August at 8.9 ± 1.5 g C m−2 but remained constant at an average of ~4 g C m−2 during other seasons. 1.5 g C m−2 but remained constant at an average of ~4 g C m−2 during other seasons. The POC concentrations had significantly positive correlations (R2 = 0.7575, *p* < 0.01 in the YS; R2 = 0.8105, *p* < 0.01 in the SS; R2 = 0.5723, *p* < 0.01 in the EJS) with the PON concentrations in this study. The average C/N ratios at each month (February, April, August, and October) were 9.2 ± 1.0 (mean ± S.D.), 7.7 ± 0.9, 9.3 ± 0.9, and 18.6 ± 2.3 in the YS; 10.8 ± 2.1, 8.1 ± 1.4, 9.2 ± 1.5, and 11.3 ± 3.0 in the SS; and 8.9 ± 1.2, 6.6 ± 0.8, 12.5 ± 0.9, and 6.3 ± 1.2 in the EJS, respectively.

The mean POC concentrations integrated in the euphotic zone in the YS, the SS, and the EJS showed different seasonal patterns in comparison to the chl-*a* concentrations (Figure 6b). The POC concentrations in the YS and SS gradually increased from February, at

, to October, with 10.4 ± 3.7 and 7.5 ± 3.1 g C m−2, respectively.

*J. Mar. Sci. Eng.* **2021**, *9*, x FOR PEER REVIEW 10 of 20

tion of micro-sized phytoplankton in the SS was observed in October (49.4 ± 19.5 %), followed by April (31.7 ± 30.5 %), August (10.9 ± 3.8 %), and February (8.4 ± 2.1 %). The fractions of nano- and pico-sized phytoplankton in the total chl-*a* concentrations in the EJS in each season were 25.4 ± 9.2 and 42.0 ± 16.6 % (February), 28.8 ± 8.3 and 43.8 ± 12.7 % (April), 26.9 ± 13.3 and 55.9 ± 16.6 % (August), and 19.0 ± 5.5 and 39.5 ± 18.0 % (October), respectively. The fraction of micro-sized phytoplankton in the EJS was gradually decreased from February (32.7 ± 23.8 %) to August (17.3 ± 11.4 %) and then increased in

**Figure 6.** Nightingale rose diagrams of mean chl-*a* (**a**) and POC (**b**) concentrations and primary productions (**c**) in the YS, the SS, and the EJS, 2018.

The POC concentrations had significantly positive correlations (R2 = 0.7575, *p* < 0.01 in the YS; R2 = 0.8105, *p* < 0.01 in the SS; R2 = 0.5723, *p* < 0.01 in the EJS) with the PON concentrations in this study. The average C/N ratios at each month (February, April, August, and October) were 9.2 ± 1.0 (mean ± S.D.), 7.7 ± 0.9, 9.3 ± 0.9, and 18.6 ± 2.3 in the YS; 10.8 ± 2.1, 8.1 ± 1.4, 9.2 ± 1.5, and 11.3 ± 3.0 in the SS; and 8.9 ± 1.2, 6.6 ± 0.8, 12.5 ± 0.9, and 6.3 ± 1.2 in the EJS, respectively.

### *3.4. Primary Production of Phytoplankton*

The primary productions of phytoplankton integrated from different six-light depths (100, 50, 30, 12, 5, and 1%) ranged from 1.0 to 135.1 (YS), 1.8 to 63.7 (SS), and 2.3 to 119.3 (EJS) mg C m−2 h −1 , respectively (Figure 7). The ranges of the primary productions in the YS and the EJS were more variable than in the SS in this study. High mean primary productions in the YS, the SS, and the EJS were observed during April (29.3 ± 39.4, 42.6 ± 7.8, and 49.1 ± 25.2 mg C m−2 h −1 ) and October (60.6 ± 17.8, 48.4 ± 15.4, and 43.3 ± 31.1 mg C m−2 h −1 ) (Figure 6c). In comparison, the mean primary productions during February and August were low in the YS (2.6 ± 1.2, 9.3 ± 1.0 mg C m−2 h −1 ), the SS (6.8 ± 3.5 and 19.5 ± 12.5 mg C m−2 h −1 ), and the EJS (10.6 ± 7.7 and 28.4 ± 20.4 mg C m−2 h −1 ) (Figure 6c). Overall, there were distinct seasonal variations in the primary productions, which were higher in spring and autumn than those in winter and summer in all waters of the littoral sea in Korea in 2018.

the SS, and the EJS, 2018.

**Figure 6.** Nightingale rose diagrams of mean chl-*a* (**a**) and POC (**b**) concentrations and primary productions (**c**) in the YS,

The primary productions of phytoplankton integrated from different six-light depths (100, 50, 30, 12, 5, and 1%) ranged from 1.0 to 135.1 (YS), 1.8 to 63.7 (SS), and 2.3 to 119.3 (EJS) mg C m−2 h−1, respectively (Figure 7). The ranges of the primary productions in the YS and the EJS were more variable than in the SS in this study. High mean primary productions in the YS, the SS, and the EJS were observed during April (29.3 ± 39.4, 42.6 ± 7.8, and 49.1 ± 25.2 mg C m−2 h−1) and October (60.6 ± 17.8, 48.4 ± 15.4, and 43.3 ±31.1 mg C m−2 h−1) (Figure 6c). In comparison, the mean primary productions during February and August were low in the YS (2.6 ± 1.2, 9.3 ± 1.0 mg C m−2 h−1), the SS (6.8 ± 3.5 and 19.5 ± 12.5

), and the EJS (10.6 ± 7.7 and 28.4 ± 20.4 mg C m−2 h−1) (Figure 6c). Overall,

there were distinct seasonal variations in the primary productions, which were higher in spring and autumn than those in winter and summer in all waters of the littoral sea in

*3.4. Primary Production of Phytoplankton*

mg C m−2 h−1

Korea in 2018.

**Figure 7.** Spatial distributions of the primary production integrated within the euphotic depth from 100 to 1% light depth in the YS, the SS, and the EJS during Feb. (**a**), Apr. (**b**), Aug. (**c**), and Oct (**d**). **Figure 7.** Spatial distributions of the primary production integrated within the euphotic depth from 100 to 1% light depth in the YS, the SS, and the EJS during Feb. (**a**), Apr. (**b**), Aug. (**c**), and Oct (**d**).

The results of PCA to determine major environmental and biological factors affecting the primary productions of phytoplankton in the YS, the SS, and the EJS throughout the observation period are shown in Figure 8a–c. The two ordination axes (PC1 and PC2) of principal components (PC) accounted for the cumulative variances of 61.6, 66.9, and 54.7% in the YS, the SS, and the EJS, respectively. Primary production in the YS was positively correlated with the chl-*a* and POC concentrations and temperature but negatively correlated with the MLD and compositions of nano-sized phytoplankton (Figure 8a). The positive relations between primary production and total chl-*a* concentrations and compositions of micro-sized phytoplankton were observed in the SS (Figure 8b). In contrast, picosized phytoplankton compositions and nutrients except for NH4 were negatively related to primary production in the SS (Figure 8b). For the EJS, the total chl-*a* concentrations, compositions of the micro-sized plankton, and salinity had positive effects, whereas the pico-sized plankton and water temperature had negative effects on the primary production (Figure 8c).

No strong correlation (R2 = 0.1225 *p* > 0.05) was found between the biomass contributions of pico-sized phytoplankton and the primary production of phytoplankton in the YS (Figure 9a). In contrast, significantly negative correlations between the biomass contributions of pico-sized phytoplankton and the primary production were observed in the SS (R2 = 0.791, *p* < 0.01) and the EJS (R2 = 0.801, *p* < 0.01) (Figure 9b,c).

*J. Mar. Sci. Eng.* **2021**, *9*, x FOR PEER REVIEW 12 of 20

The results of PCA to determine major environmental and biological factors affecting the primary productions of phytoplankton in the YS, the SS, and the EJS throughout the observation period are shown in Figure 8a–c. The two ordination axes (PC1 and PC2) of principal components (PC) accounted for the cumulative variances of 61.6, 66.9, and 54.7 % in the YS, the SS, and the EJS, respectively. Primary production in the YS was positively correlated with the chl-*a* and POC concentrations and temperature but negatively correlated with the MLD and compositions of nano-sized phytoplankton (Figure 8a). The positive relations between primary production and total chl-*a* concentrations and compositions of micro-sized phytoplankton were observed in the SS (Figure 8b). In contrast, picosized phytoplankton compositions and nutrients except for NH4 were negatively related to primary production in the SS (Figure 8b). For the EJS, the total chl-*a* concentrations, compositions of the micro-sized plankton, and salinity had positive effects, whereas the pico-sized plankton and water temperature had negative effects on the primary produc-

The results of PCA to determine major environmental and biological factors affecting the primary productions of phytoplankton in the YS, the SS, and the EJS throughout the observation period are shown in Figure 8a–c. The two ordination axes (PC1 and PC2) of principal components (PC) accounted for the cumulative variances of 61.6, 66.9, and 54.7 % in the YS, the SS, and the EJS, respectively. Primary production in the YS was positively correlated with the chl-*a* and POC concentrations and temperature but negatively correlated with the MLD and compositions of nano-sized phytoplankton (Figure 8a). The positive relations between primary production and total chl-*a* concentrations and compositions of micro-sized phytoplankton were observed in the SS (Figure 8b). In contrast, picosized phytoplankton compositions and nutrients except for NH4 were negatively related to primary production in the SS (Figure 8b). For the EJS, the total chl-*a* concentrations, compositions of the micro-sized plankton, and salinity had positive effects, whereas the pico-sized plankton and water temperature had negative effects on the primary produc-

**Figure 8.** Principal components analysis (PCA) ordination plots showing primary production of phytoplankton in relation to environmental and biological conditions in the YS (**a**), the SS (**b**), and the EJS (**c**), 2018. Micro-, nano-, and pico- represent contributions of compositions of micro-, nano-, and pico-sized phytoplankton to total chl-*a*; PP represents primary production. **Figure 8.** Principal components analysis (PCA) ordination plots showing primary production of phytoplankton in relation to environmental and biological conditions in the YS (**a**), the SS (**b**), and the EJS (**c**), 2018. Micro-, nano-, and picorepresent contributions of compositions of micro-, nano-, and pico-sized phytoplankton to total chl-*a*; PP represents primary production. tions of pico-sized phytoplankton and the primary production of phytoplankton in the YS (Figure 9a). In contrast, significantly negative correlations between the biomass contributions of pico-sized phytoplankton and the primary production were observed in the SS (R2 = 0.791, *p* < 0.01) and the EJS (R2 = 0.801, *p* < 0.01) (Figure 9b,c).

**Figure 9.** Relationships between primary production and contribution of pico-sized phytoplankton (<2 m) to total chl-*a* concentrations in the YS (**a**), the SS (**b**), and the EJS (**c**), 2018. **Figure 9.** Relationships between primary production and contribution of pico-sized phytoplankton (<2 µm) to total chl-*a* concentrations in the YS (**a**), the SS (**b**), and the EJS (**c**), 2018.

### **4. Discussion**

tion (Figure 8c).

tion (Figure 8c).

### *4.1. Comparisons of Primary Production between This and Previous Studies*

**Figure 9.** Relationships between primary production and contribution of pico-sized phytoplankton (<2 m) to total chl-*a* concentrations in the YS (**a**), the SS (**b**), and the EJS (**c**), 2018. Based on a 10-h photoperiod and the hourly primary productions obtained in this study (Figure 6), the mean daily primary productions in the YS were 25.8 ± 11.9, 292.7 ± 393.9, 139.0 ± 66.9, and 607.5 ± 172.6 mg C m−2 d −1 during winter, spring, summer, and autumn, respectively. Our values obtained in this study were slightly lower than the ranges (56–947 mg C m−2 d −1 ) of the values reported previously in adjacent or nearly identical regions to our sites in the YS (Table 3). In particular, our spring (293 mg C m−2 d −1 ) and summer (139 mg C m−2 d −1 ) values in this study were significantly lower (*t*-test, *p* < 0.05) than the spring (851 ± 108 mg C m−2 d −1 ) and summer (555 ± 231 mg C m−2 d −1 ) values averaged from previous studies. These lower seasonal productions in 2018 might be explained by a recent change in the nutrient budgets in the YS. An increasing trend in dissolved inorganic nitrogen (DIN) concentration since the 1980s was reported, whereas a decreasing trend from the 1980s to 2000 followed by a slight increase in PO4 concentration was observed in the YS [16,36]. These changes in DIN and PO4 have induced a gradual increase in the N/P ratio and a shift from N-limitation to P-limitation in the YS [36]. The P-limited condition could convert dominant species of phytoplankton from diatoms to small-sized non-diatoms with higher growth rates in P-limited waters but lower photosynthetic efficiencies [18,24,37]. Lin et al. [16] reported that a dramatic decrease in primary production in the YS during all seasons between 1983–1986 and 1996–1998 periods could

be one of the ecological responses caused by the increase in the N/P ratio. In this study, the N/P ratios (32 ± 14) during the spring period were significantly higher (one-sample *t*-test, *p* < 0.01) than the Redfield ratios (16) [38], which could have resulted in a limitation for diatom growth [39,40]. Indeed, the diatom compositions (approximately 50%) in the YS in spring based on the results from our parallel study (non-published data) were distinctly lower than those reported previously in 1986 (89%) and 1998 (70%) [23]. This shift in dominant species could have caused the low primary production in spring 2018. Jang et al. [24] reported that the high contribution of pico-sized (<2 µm) phytoplankton to the total primary production could induce a lower total primary production in the YS when the N/P ratio is higher than 30 during the summer period. We did not measure the production of pico-sized phytoplankton in this study, but the higher N/P ratio (54 ± 78) at upper euphotic depth (100 and 30%) accounted for about 75% of integrated primary production and could explain the lower primary production in the YS during summer 2018.

Since the primary production measurements have rarely been conducted in the SS section belonging to the northern part of the East China Sea, we compared our results with those measured previously in the entire East China Sea (Table 4). The average daily primary productions in the SS during this observation are within the range (102–1727 mg C m−2 d −1 ) reported previously in the East China Sea (Table 4). However, the winter and summer values in this study were significantly lower (*t*-test, *p* < 0.05) than the mean winter (206 ± 93 mg C m−2 d −1 ) and summer (621 ± 179 mg C m−2 d −1 ) productions reported previously. In comparison, the autumn value (487 mg C m−2 d −1 ) in this study was consistent with the previous findings (503 ± 186 mg C m−2 d −1 ). For the springtime, our daily production (426 mg C m−2 d −1 ) was not statistically different (*t*-test, *p* > 0.05) from the mean production (350 ± 161 mg C m−2 d −1 ) in early spring (March), but our spring value was considerably lower than those reported previously in April (1727 mg C m−2 d −1 ) and May (1375 mg C m−2 d −1 ).

**Table 3.** Comparisons of daily primary production in the YS. PP represents daily primary production.

**Table 4.** Comparisons of daily primary production in the East China Sea. PP represents daily primary production.

The daily primary productions measured in this study during four seasons are within the range (44–1505 mg C m−2 d −1 ) obtained previously from the various regions in the EJS in different seasons (Table 5). However, our value (284 mg C m−2 d −1 ) during the winter period was significantly higher (*t*-test, *p* < 0.05) than the winter mean value (75 ± 44 mg C m−2 d −1 ) reported by Nagata [51] and Yoshie et al. [52], whereas our spring (491 mg C m−2 d −1 ) and summer (106 mg C m−2 d −1 ) rates were significantly lower (*t*-test, *p* < 0.05) than the spring (858 ± 376 mg C m−2 d −1 ) and summer (519 ± 184 mg C m−2 d −1 ) values averaged from various previous studies. A plausible mechanism for the difference might be related to the development of the MLD in the EJS during the wintertime. A vigorous vertical mixing driven by the Asian winter monsoon can limit the availability of light to phytoplankton in winter [53,54] but induces an increase in the nutrient availability in the upper euphotic layer from spring to summer [55,56]. However, the MLD has been gradually decreased by an increase in water temperature and weakened wind stress in the EJS [17,57,58], which could offer better light conditions for phytoplankton growth in winter but fewer nutrients for the spring phytoplankton bloom. In this way, the difference in seasonal primary production in the EJS mentioned above could be explained by the recent change in the MLD. However, because our surveys in the EJS were restricted to only 2018, this mechanism needs to be verified by a long-term observation. Another reason for the low primary production, especially in spring 2018, could be potentially having missed the bloom timing of the phytoplankton during our observation period. In general, the spring bloom in the EJS is mainly driven by the massive growth of diatoms, which account for the majority of large-sized (> 20 µm) phytoplankton [59–61]. Indeed, Kwak et al. [62] observed a significantly higher contribution (approximately 60%) of diatoms during the spring bloom period than in other seasons. In this study, the contribution of the large-sized phytoplankton was rather lower during the spring (Figure 5c). However, much lower diatom contributions were detected based on our parallel stud, showing that diatoms accounted for only 23.1% (± 9.9%) of total phytoplankton communities in the EJS in spring (non-published data). The other reason might be conspicuously low phytoplankton biomass in the EJS in April 2018. Based on MODIS (Moderate Resolution Imaging Spectrometer)-Aqua monthly level-3

datasets regarding chl-*a* (https://oceandata.sci.gsfc.nasa.gov/MODIS-Aqua/, accessed on 3 August 2021), the surface chl-*a* showed strong negative anomalies in the southwestern part of the EJS during April between 2003–2015 and 2018 (data not shown). As the chl-*a* concentrations in the EJS were one of the major factors controlling the primary production (Figure 8c), a noticeable low chl-*a* concentration could cause lower primary production in the EJS during the springtime in 2018. At the current stage, it is difficult to find out a solid reason for the low chl-*a* concentration in the EJS during April 2018, which should be further resolved for a better understanding of the EJS ecosystem.

**Table 5.** Comparisons of daily primary production in the EJS. PP represents daily primary production.

### *4.2. Main Factors Affecting the Primary Production in the YS, SS, and EJS in 2018*

Based on the PCA results (Figure 8), the major factors controlling the phytoplankton productions were different among the three seas. Total chl-*a* concentrations (positively; +), temperature (+), MLD (negatively; −), and nano-sized phytoplankton contribution (−) are found to be major controlling factors in the YS. In comparison, total chl-*a* concentrations (+), pico- (−) and micro-sized (+) phytoplankton contributions, and nutrients (−) except for NH4 can greatlyaffect the primary production in the SS. For the EJS, the primary production of phytoplankton can greatly vary due to total chl-*a* concentrations (+), micro-sized phytoplankton contribution (+), salinity (+), pico-sized phytoplankton (−), and water temperature (−). The effects of physical (temperature, salinity, and MLD) and chemical (nutrients) factors are different in the YS, the SS, and the EJS. Given the positive relationships between the primary productions and the total chl-*a* concentrations in this study, biomass-driven primary productions are characteristics in the YS, the SS, and the EJS ecosystems, at least in 2018. However, the effects of the three size groups of phytoplankton can be different among the three seas. The contribution of nano-sized phytoplankton in the YS and the contributions of pico-sized phytoplankton in the SS and the EJS are negatively correlated with the primary productions in this study. Choi et al. [42] reported nano-phytoplankton contributed greatly to the primary production in the YS,

based on the large biomass contribution of nano-phytoplankton (approximately 60%). In this study, the negative relationship between the nano-sized phytoplankton contribution and the primary production indicates that increasing contributions of the nano-sized phytoplankton could decrease the primary production in the YS. In the EJS, several previous studies reported higher contributions of pico-sized phytoplankton could cause a decrease in the primary production [12,22,69]. Indeed, marked decreasing trends in the primary productions with increasing pico-sized phytoplankton biomass were observed in the SS and EJS during our observation period in 2018 (Figure 9b.c). This could be caused by the different primary productivities between pico- and large-sized (>2 µm) phytoplankton [22]. Generally, pico-sized phytoplankton have a lower primary productivity than large phytoplankton [14,22,70]. Therefore, the total primary production can be decreased by increasing contribution of pico-sized phytoplankton, with their lower productivity traits. Under ongoing warming ocean conditions, pico-sized phytoplankton are expected to be predominant in phytoplankton communities [71–74]. In this pico-sized-phytoplanktondominated ecosystem, a lower total primary production could be expected in the SS and the EJS based on the negative relationships between the primary production and pico-sized phytoplankton observed in this study. The ecological roles of pico-sized phytoplankton in regional marine ecosystems should be further investigated in the YS, the SS, and the EJS with current environmental changes.

**Author Contributions:** Conceptualization, H.-K.J. and S.-H.L.; methodology, H.-K.J.; validation, H.-K.J. and S.-H.L.; formal analysis, H.-K.J.; investigation, H.-K.J., Y.K., H.J., J.-J.K., D.L., N.J., K.K., M.-J.K. and S.K.; data curation, H.-K.J.; writing—original draft preparation, H.-K.J.; writing—review and editing, S.-H.L.; visualization, H.-K.J.; supervision, S.-H.L.; project administration, S.-H.Y. and S.-H.L.; funding acquisition, S.-H.Y. and S.-H.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was fundedby the National Institute of Fisheries Science ('Development of marine ecological forecasting system for Korean waters'; R2021068).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** We appreciate the captains and crew of R/V Tamgu 3 and 8 for their assistance in collecting our samples. We would also like to thank the researchers in the NIFS for their assistance with sample analysis. This work was supported by the National Institute of Fisheries Science (NIFS) grant ('Development of marine ecological forecasting system for Korean waters'; R2021068) funded by the Ministry of Oceans and Fisheries, Republic of Korea.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**


**1. Introduction**

Neuromorphic computing, technology that mimic neuro-biological architectures present in the nervous system using electronic circuits, has attracted attention as next-generation computing due to its characteristics as they can process complex data with high efficiency, high speed and low power consumption. Neuromorphic computing importance increased in the industry because it efficiently executes artificial intelligence algorithms by imitating the brain nerve structure of humans. The conventional von Neumann computing using separated processors and memory systems is not efficient for machine learning due to processor-memory bottlenecks. Because the machine learning has a special workload that iterates simple computation with alot of data, there should be huge data traffic between processors and memory. For neuromorphic computing system, it consists of multiple neurons and synapses to compute and store data, and a neural network to communicate them. Therefore, this computing system can compute simple iterations efficiently for the training of machine learning. This chapter will discuss neuromorphic computing goals and challenges, showing how materials, devices, algorithms development researches lead to high expectations in neuromorphic computing field. We will discuss neuromorphic systems that are already existing today and expectations for what neuromorphic computing can achieve in the future.