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ISSN : 1226-9999(Print)
ISSN : 2287-7851(Online)
Korean J. Environ. Biol. Vol.40 No.4 pp.374-386

Spatial distribution of phytoplankton in Gamak Bay in spring, with emphasis on small phytoplankton

Yeongji Oh, Yoonja Kang*
Department of Ocean Integrated Science, Chonnam National University, Yeosu 56828, Republic of Korea
* Corresponding author Yoonja Kang Tel. 061-659-7141 E-mail.
22/08/2022 25/10/2022 28/10/2022


Phytoplankton communities, with emphasis on picoplankton and nanoplankton, were investigated in Gamak Bay, South Korea, where freshwater input and coastal water intrusion shape ecosystem functions. Shellfish farms and fish farms are located in the inner bay and outer bay, respectively, and tides translocate uneaten food and urine production from aquaculture farms toward the inner bay. Water masses were distinctly different based on a significantly different density between the surface and bottom layer and among three water masses, including the inner bay, outer bay, and Yeosu Harbor. Phytoplankton communities were quantified using flow cytometry and size-fractionated chlorophyll-a (chl-a) was measured. Salinity was a principal variable separating phytoplankton communities between the surface and bottom layer, whereas Si(OH)4 controlled the communities in the inner bay, and NH4+ and PO43- governed the outer bay communities. While phycocyanin-containing (PC) cyanobacteria dominated in the outer bay, phycoerythrin-containing (PE) cyanobacteria dominance occurred with cryptophyte dominance, indicating that nutrients affected the distribution of pico- and nanoplankton and that cryptophytes potentially relied on a mixotrophic mode by feeding on PE cyanobacteria. Interestingly, picoeukaryotes and eukaryotes larger than 10 μm were mostly responsible for the ecological niche in the western region of the bay. Given that chl-a levels have historically declined, our study highlights the potential importance of increased small phytoplankton in Gamak Bay. Particularly, we urge an examination of the ecological role of small phytoplankton in the food supply of cultivated marine organisms.



    Phytoplankton are responsible for more than 50% of primary production in coastal waters across the globe as they trigger the biogeochemical cycles and transfer carbon energy through marine food webs (Cloern et al. 2014). Particularly, anthropogenic activity-driven nutrient loadings (Paerl et al. 2014) and excessive nutrient input from aquaculture farms (Bouwman et al. 2013b) cause eutrophication, thereby governing phytoplankton biomass and species composition in communities. The eutrophication often results in harmful algal blooms, which disrupt ecosystem functions in coastal waters (Glibert et al. 2018). While during this century eutrophication driven by human activities has been a central problem in coastal ecosystems (Sinha et al. 2017), an amount of nutrients originating from aquaculture farms is substantial (Bouwman et al. 2013b) and the extent which the nutrients exacerbate coastal ecosystems will increase six times by 2050 (Bouwman et al. 2013a).

    Dominance of small phytoplankton such as picoplankton and nanoplankton often occur in the estuarine ecosystems because of reduced river discharge and relatively more effects of sewage treatment plants (Kim et al. 2019;Kang et al. 2020). Although picoplankton actively proliferate in oligotrophic and high temperature environment, where they can be superior to microplankton (Raven 1998;Agawin et al. 2000), a variety of picoplankton including autotrophic cyanobacteria and picoeukaryotes also comprise of major phytoplankton communities in coastal waters (Worden et al. 2004;Wu et al. 2014;Kang et al. 2015;Kang and Oh 2021). In Gamak Bay located in Yeosu Channel, which connects Gwangyang Bay and the South Sea, freshwater flowing into the bay through the northwestern channel and nutrient loadings from Seomjin River and Yeondeung Creek substantially contribute to nutrient components. In addition, shellfish farms in the northern part of the bay, oyster farms in the central part of the bay, and fish farms in the southern channel of the bay are another major point sources of nutrients (Lee et al. 2020). Given that prevailing of various aquaculture farms throughout the bay, nutrient input from adjacent aquaculture farms may not be negligible.

    Multiple studies of the spatial and temporal phytoplankton distribution have been investigated in Gamak Bay for the last decade. Phytoplankton communities were identified using high performance liquid chromatography with CHEMTAX analysis (Oh et al. 2008a) and the phytoplankton distribution was described with environmental variations (Yoon 2000;Oh et al. 2009). Kwon et al. (2011) revealed that the activity of alkaline phosphatase and alkaline phosphatase-hydrolysable phosphorus is of importance for phytoplankton to acquire an ecological niche. Studies of phytoplankton ecology in Gamak Bay have mostly focused on microplankton such as diatoms or dinoflagellates that are observable with microscopy but no studies of picoplankton or nanoplankton ecology have been performed. Conventionally, picoplankton are abundant in the open sea relying on regenerated nutrients and microplankton dominate in eutrophic coastal waters (Cloern 2018). Recently, coastal waters have shifted towards environments where picoplankton and nanoplankton can be favored (Ansotegui et al. 2003;Kang et al. 2019, 2020).

    Thus, we first investigated the spatial distribution of size-fractionated phytoplankton (picoplankton, nanoplankton, and microplankton) with physical-chemical variables in Gamak Bay, to enlighten the importance of small phytoplankton in the coastal ecosystems, particularly adjacent to aquaculture farms. Size-fractionated chlorophyll-a (chl-a) and phytoplankton groups quantified using flow cytometry were focused using the multivariate analyses.


    1. Study region

    Gamak Bay is located around the archipelago off southwestern Korea, and is a semi-enclosed bay connecting between Yeosu Hwayang-myeon and Dolsan-eup with a length of 15 km and area of 112 km2 (Fig. 1). We investigated five stations, where the Korea Marine Environment Management Corporation (KOEM) have seasonally and regularly monitored for a decade to evaluate the marine ecology and environmental conditions, and the National Institute of Fisheries Science (NIFS) has monitored to estimate water quality associated with the dominant aquaculture farms. Three distinct water masses included the northern inner bay water (here after, the inner bay), Yeosu harbor water in the northern narrow channel influenced by Seomjin River and Yeondung Creek (Yeosu harbor), and the southern outer bay water (the outer bay) (Lee and Cho 1990). During flood tide, a substantial amount of seawater intrudes through the southern channel of the bay and during ebb tide, the bay waters escape via the northeastern channel and the southern channel (Kim et al. 2012). Exogenous matter and tides affect the three water masses (i.e., inner bay, Yeosu harbor, and outer bay) while the northern region in the bay experiences eutrophication due to considerable accumulation of organic matter (Yoon 2000). In addition, in the northern region of Gamak Bay, hypoxic water masses frequently occur in summer (Lee and Cho 1990;Lee 1992;Kim et al. 2006, 2010;Kim et al. 2011). These characteristics of the northern waters can affect the center of the bay.

    2. Field survey

    Samples were collected at five stations on May 28-29, 2021 during a cruise with R/V Cheongkyung using a Niskin water sampler (General Oceanics, Miami, FL, USA) from the surface and bottom layers at 1 m below the water surface and 1 m above the surface sediments, respectively (Fig. 1). Samples for chl-a, flow cytometry, and dissolved inorganic nitrogen (NO3-, NO2-, NH4+, PO43-, and Si(OH)4) were generated on board. Temperature, salinity, dissolved oxygen (DO), and pH were measured on the surface water using YSI ProDSS (YSI Inc., Yellow Springs, OH, USA) while the physical variables of the bottom water were obtained from the Marine Environment Information System (www. due to lack of data. Transparency was measured using a secchi disk. For total chl-a, the collected seawater was filtered onto GF/F (47 mm in diameter; Whatman plc, Maidstone, UK) and the size-fractionated chl-a samples were generated by sequentially filtering onto polycarbonate filters with different pore sizes (20 μm polycarbonate track etched membrane filter 47 mm diameter, GVS North America, ME, USA; 10 μm polycarbonate membrane filter Isopore plc; GF/F filter Whatman plc, Maidstone, UK). Because picoplankton with a size of 3 μm or larger were often observed (Kang et al. 2015), 10 μm filters was used to define small phytoplankton including pico- and nanoplankton. Flow cytometric samples were prepared by adding 0.5 mL of 10% buffered formalin into 4.5 mL of seawater and storing at -80°C until analysis. Adding 1-2% final concentration buffered formalin considers the most optimal preservation method for phytoplankton community analysis when flow cytometry is applied (Poulton and Martin 2010). 20 mL of seawater were filtered onto pre-combusted GF/F filters (25 mm, 450°C for 2 h) to produce dissolved inorganic nitrogen samples and were stored at -80°C until analysis.

    3. Sample analysis

    Size-fractionated chl-a was measured using a fluorometer (TrilogyTM fluorometer; Turner Designs, Sunnyvale, CA, USA) after samples were added with 90% acetone and extracted at -80°C for 24 h. Small plankton including picoplankton<10 μm and nanoplankton 10-20 μm, and microplankton>20 μm were analyzed. With targeting phytoplankton<20 μm, size-fractionated phytoplankton communities were quantified using a flow cytometer (BD AccuriC6TM Becton, Dickinson and Company Franklin Lakes, NJ, USA) at the National Institute of Fisheries Science (NIFS) in Yeosu, South Korea. Eukaryotes (>2 μm), picoeukaryotes, phycocyanin-containing cyanobacteria (PC cyanobacteria), phycoerythrin-containing cyanobacteria (PE cyanobacteria), and cryptophytes were identified according to (Kang et al. 2015) and Kang and Oh (2021). Particle sizes were calibrated using Sphero fluorescence- calibrated glass beads (2 μm; RFP-20-5; Spherotech, Lake Forrest, IL, USA). Dissolved inorganic nutrients were analyzed in duplicate using a SEAL QuAAtro Auto Analyzer (Seal Analytical Ltd., Southampton, UK) at NIFS ( Jones 1984;Parsons et al. 1984;Price and Harrison 1987).

    4. Data analysis

    Euphotic depth (Zp) was calculated according to Cloern (1987) that shows an equation of Zp=4.61 Kd-1, where Kd is light extinction coefficient (Kd=1.7 Zs-1) and Zs is transparency measured with a secchi disk. The transparencydriven euphotic depth has been applied to global coastal waters and to Korean coastal waters like Gwangyang Bay (Lee et al. 2017;Kang et al. 2020). The ‘seacarb’ package in a R version 3.6.3 (R Foundation for Statistical Computing, Vienna, Austria) was used to calculate density of three water masses, and a Kruskal-Wallis test with Dunn’s test was performed to compare the density of the water masses. To determine the most influential environmental variables to this ecosystem, ‘bio-env’ analysis was performed, and then using the results, the effects of the variables on the distribution of major phytoplankton groups were assessed through the redundancy analysis (RDA). Correlation analysis (CA) was conducted to exhibit the correlation strength between the important variables and size-fractionated phytoplankton groups. t-test was performed to compare the difference in environmental variables between the surface and bottom layers. Statistics were performed using R with packages including ‘vegan’ for bioenv and RDA (Oksanen et al. 2013) and ‘corrplot’ for CA (Wei et al. 2017). Plots were generated using a R package of ‘ggplot2’ (Wickham 2016) or Ocean Data View (Schlitzer 2015).


    1. Spatial distribution of environmental variables

    Three water masses were identified as the inner bay, outer bay, and Yeosu Harbor, and the surface and bottom layers were distinct (Table S1). Surface water temperature ranged from 19 to 21.2°C (mean 20.3°C, Fig. 2A) and bottom water temperature ranged from 16.4 to 18.5°C (mean 17.8°C; Fig. 2F). The lowest temperature on the surface water was at St. 1 (19°C), and water temperature was higher in the inner bay (Fig. 2A). Bottom water temperature distributed similar to the surface water temperature but lowered by mean 2.4°C (Fig. 2F;p<0.05; t-test). Mean surface salinity was 34.6 psu with a range of 34.0-35.6 psu (Fig. 2B) and mean bottom salinity was 33.2 psu with a range of 32.9-33.5 psu (Fig. 2G). Surface salinity was highest in the outer bay and lowest near Yeosu harbor (Fig. 2B) while bottom salinity had no discernible difference among stations (Fig. 2G). The salinity difference between the surface and bottom layers was significant (p<0.05; t-test). DO ranged 6.8-7.4 mg L-1 (mean 7.1 mg L-1) in the surface layer (Fig. 2C) and 7.3-8.3 mg L-1 (mean 7.9 mg L-1) in the bottom layer (Fig. 2H). The spatial difference in DO levels was higher in the bottom layer than in the surface layer (p< 0.05; t-test). In the surface layer, DO concentration was highest in the inner bay (7.4 mg L-1) and decreased toward Yeosu harbor (6.8 mg L-1) and the outer bay (7.0 mg L-1, Fig. 2C). However, in the bottom layer, the outer bay had high DO concentration (8.3 mg L-1, Fig. 2H). The pH in the range of 8.10 to 8.19 showed an average of 8.17 and 8.14 in the surface layer and the bottom layer, respectively. The pH was relatively stable compared to other environmental variables (p>0.05; t-test). The pH was high in the inner bay (8.19) and declined toward the outer bay (8.16) and Yeosu harbor (8.13; Fig. 2D) in the surface layer while it was high in the outer bay (8.17) and low in the inner bay in the bottom layer (8.11; Fig. 2I). Euphotic depth varied from 5.6 to 9.2 m (mean 7.5 m) being the lowest in the outer bay (5.6 m) and deepening toward land (Fig. 2E).

    2. Spatial distribution of dissolved inorganic nutrients

    NO3- levels varied from 0.53-3.24 μm (mean 1.62 μm) in the surface layer (Fig. 3A) and from 0.20-2.66 μm (mean 1.35 μm) in the bottom layer (Fig. 3E). NO3- levels were relatively high near Yeosu harbor with 3.24 μm in the surface layer and 2.56 μm in the bottom layer and the outer bay with 2.61 μm in the surface layer and 2.66 μm in the bottom layer (Fig. 3A, E). While NH4+ levels were similar between the surface layer (mean 1.50 μm) and the bottom layer (mean 1.49 μm) with a range from 0.52 to 3.28 μm and from 0.64 to 3.06 μm, respectively (Fig. 3B, F), spatially the levels were high near Yeosu harbor (3.38 μm) relative to other region (Fig. 3B, F). PO43- levels varied from 0.11- 0.32 μm (mean 0.21 μm) in the surface layer (Fig. 3C) and from 0.11 to 0.36 μm (mean 0.23 μm) in the bottom layer (Fig. 3G) exhibiting the similar levels between two layers and being higher near Yeosu harbor (0.32 μm) and the outer bay (0.31 μm; Fig. 3C, G). Si(OH)4 levels ranged from 10.66 to 13.77 μm (mean 12.10 μm) in the surface layer (Fig. 3D) and from 10.60 to 14.49 μm (mean 12.32 μm) in the bottom layer (Fig. 3H) with the opposite pattern that the highest level was detected in the outer bay in the surface layer (13.77 μm; Fig. 3D) but in the inner bay in the bottom layer (13.39 μm; Fig. 3H). The concentration of dissolved inorganic nutrients was not significantly different between the surface and bottom layers (p>0.05 for NO3-, NH4+, PO4-, Si(OH)4; t-test).

    3. Spatial distribution of phytoplankton biomass and community

    Total phytoplankton biomass (chlorophyll-a) varied from 1.88 μg L-1 in the outer bay to 3.66 μg L-1 in the inner bay in the surface layer, whereas each size-fractionated phytoplankton biomass appeared the different spatial distributions (Fig. S1). The microplankton biomass ranged from 1.42 μg L-1 in the outer bay to 1.85 μg L-1 in the inner bay in the surface layer (Fig. S1B) while the nanoplankton biomass was high in the Yeosu harbor (1.04 μg L-1) and declined toward the outer bay (0.6 μg L-1) and the inner bay (0.51 μg L-1, Fig. S1C). The picoplankton biomass ranged from 0.74 μg L-1 in the Yeosu harbor to 1.34 μg L-1 in the inner bay in the surface layer (Fig. S1D).

    Phytoplankton communities quantified using a flow cytometer were examined. Abundance of eukaryotes>2 μm was 45-120 cells mL-1 (mean 85 cells mL-1) in the surface layer (Fig. 4A) while that was 40-133 cells mL-1 (mean 72 cells mL-1) in the bottom layer (Fig. 4F). In the surface layer, eukaryotes>2 μm were detected near Yeosu harbor with mean 120 cells mL-1 and in the inner bay with mean 67 cells mL-1 (Fig. 4A), and the abundance was slightly higher in the bottom layer (mean 133 cells mL-1; Fig. 4F). Picoeukaryote abundance ranged from 1,035 to 2,165 cells mL-1 (mean 1,477 cells mL-1) with the highest abundance in the inner bay (mean 2,163 cells mL-1) in the surface layer (Fig. 4B) while the abundance varied from 785 to 1,955 cells mL-1 (mean 1,458 cells mL-1) with the highest abundance in the western bay (mean 1,955 cells mL-1) in the bottom layer (Fig. 4G). PC cyanobacteria abundance was much higher in the bottom layer (29,450-84,875 cells mL-1; mean 49,651 cells mL-1; Fig. 4H) than in the surface layer (31,958-50,610 cells mL-1; mean 40,356 cells mL-1; Fig. 4C), and the abundance sharply increased to 84,875 cells mL-1 in the outer bay (Fig. 4H). PE cyanobacteria abundance was relatively low compared to PC cyanobacteria abundance with mean 532 cells mL-1 in the surface layer (Fig. 4D) and mean 276 cells mL-1 in the bottom layer (Fig. 4I). Cryptophyte abundance was higher in the surface layer (mean 212 cells mL-1; Fig. 4E) than in the bottom layer (mean 90 cells mL-1; Fig. 4J). Abundance of PE cyanobacteria and cryptophytes increased toward Yeosu harbor in the surface layer (Fig. 4D, E) and the outer bay in the bottom layer, whereas the abundance was relatively low in the center of the bay (Fig. 4I, J).

    4. Relationship between phytoplankton community and environmental variables

    Bio-env analysis using a ‘vegan’ package on R revealed that salinity, DO, NH4+, PO43-, and Si(OH)4 were the most influential factors in the study region (correlation strength= 0.2087; Table 1). Using those environmental variables, RDA was performed to examine the relationship between environmental variables and phytoplankton communities (Fig. 5). The spatial distribution of phytoplankton communities was mainly characterized by salinity, which clearly segregated the surface and bottom layers. While the inner bay communities were mainly correlated with Si(OH)4, the outer bay communities were largely associated with PO43- and NH4+.

    Correlation analysis showed that eukaryotes>2 μm were strongly positively correlated to euphotic depth (correlation coefficient=0.57; Fig. 6). Picoeukaryotes were positively associated with temperature (0.43) and euphotic depth (0.44) while there were strong negative correlations between picoeukaryotes and dissolved inorganic nutrients (NH4+= -0.76, NO3-= -0.81, and PO43-= -0.80; Fig. 6). Autotrophic cyanobacteria were related with physical parameters; PC cyanobacteria and PE cyanobacteria were negatively correlated to temperature ( -0.40) and DO (- 0.42), respectively, but PE cyanobacteria were positively correlated to salinity (0.57; Fig. 6). In the meantime, cryptophytes were positively associated with salinity (0.53), NO3- (0.41), and PO43- (0.32) but negatively associated with DO (- 0.43; Fig. 6).


    1. Environmental characteristics of Gamak Bay in Spring

    Water masses were separated into three as the inner bay, outer bay, and Yeosu Harbor, and the surface and bottom layers were distinct. In particular, the density difference was intensive between the inner bay and outer bay, where freshwater input and offshore seawater input contribute to characterizing the density disparity. Interestingly, bottom salinity was slightly lower than that in the surface waters during the study period. We thoughtfully performed a literature review and searched a source of freshwater, however, unfortunately, we did not find a specific reason for this. In summer, a hypoxic water mass appears in the northern Gamak Bay with low DO and pH (Kim et al. 2011). Although pH in the bottom layer of the inner bay was slightly lower than that in the outer bay and the surface layer, DO remained above 2 mg L-1, implying that DO was low but oxygendeficient water mass was not found (Kim et al. 2010).

    Light penetration was enhanced in the inner bay and the relatively shallow euphotic depth was confined to the outer bay. Higher light extinction coefficient in the outer bay indicates that the outer bay had higher suspended matter. This is well aligned with the location of the long-lined oyster farms and fish cages shown as the crosshatch pattern and diagonal hatch pattern, respectively, in Fig. 1. Fish farms usually release more organic matter that originate from uneaten food and fecal and urinary products (Bouwman et al. 2013b), and tides can translocate the suspended matter in the bay (Lee 2013). Thus, unbroken organic matter from the fish farms moving inward by tides could reduce the euphotic depth in the ecosystem.

    Levels of NO3-, NH4+, and PO43- were high in the outer bay and Yeosu Harbor, whereas Si(OH)4 levels were high in the inner bay. Gamak Bay experiences freshwater input from Yeondeung Creek and Seomjin River (Lee 1992) and outflow from a sewage treatment plant loads to the northern inner bay (Oh et al. 2008b). This indicates that high levels of nutrients in the bay originating from river discharge and sewage treatment plant can synergistically affect the phytoplankton growth and biomass in this ecosystem, in addition with the aquaculture farms in the outer bay. Oh et al. (2009) illustrated that diatoms were the most dominant phytoplankton group in Gamak Bay due to high levels of Si(OH)4. Because phytoplankton communities were not quantified using a light microcopy or HPLC in this study, we do not firmly conclude the contribution of diatoms to the major phytoplankton groups. However, we suggest that the importance of small phytoplankton should be considered and small phytoplankton were responsible for relatively high composition of phytoplankton communities in Gamak Bay.

    2. The spatial distribution of small phytoplankton in Gamak Bay

    Regenerated nutrients from uneaten food and urinary production of the aquaculture farms (i.e., fish farms) might enable phytoplankton to access to the available nutrients. Particularly, phytoplankton whose growth is favored by the reduced forms of nutrients (e.g., NH4+ and Urea) can potentially dominate in an ecosystem (Gobler et al. 2013;Kang et al. 2021;Kang and Kang 2022). In addition, the role of NH4+ increases as nutrients originating from sewage treatment plants take a large portion of bioavailable nutrients (Kang et al. 2020). The nutrients from the outer bay likely affected the spatial distribution of PC cyanobacteria while nutrients from Yeosu Harbor likely governed the distribution of PE cyanobacteria. In a coastal ecosystem, PC cyanobacteria can exist at a background level even when other picoplankton are dominant and then the picoplankton blooms can succeed to PC cyanobacteria blooms (Kang et al. 2015). Likewise, the Gamak Bay ecosystem has a potential for the cyanobacteria proliferation. While picoprokaryotes were positively correlated with nutrients as described above, picoeukaryotes were negatively correlated with most dissolved inorganic nutrients but Si(OH)4. Given that the pico-sized diatoms are detected in the ocean (Rii et al. 2018), and that another silicate-dependent picoeukaryote, Bolidophyceae, is widespread from tropics to the pole (Kuwata et al. 2018), Si(OH)4 could control the distribution of picoeukaryotes in Gamak Bay.

    Nanoplankton, cryptophytes mainly contributed to phytoplankton community in the surface layer of Yeosu Harbor and in the bottom layer of the outer bay and were negatively correlated with euphotic depth. Cryptophytes can endure the low light intensity in turbid waters, where other phytoplankton are not usually able to obtain optimal growth (Barone and Naselli-Flores 2003), and the cryptophyte abundance is often reduced with water quality improvement (Kang et al. 2021). The cryptophyte dominance was concomitant with the PE cyanobacteria dominance in the outer bay and Yeosu Harbor, indicating the potential ability of cryptophytes for the mixotrophic nutrient acquisition by consuming PE cyanobacteria (Roberts and Laybourn- Parry 1999;Yoo et al. 2017). PC and PE cyanobacteria abundance decreased with the euphotic depth increase, whereas large eukaryote abundance increased with the euphotic depth increase. PC and PE cyanobacteria can exist under a light-deficient condition, in which the water transparency is less than 0.3 m (Gobler et al. 2013). Likewise, the high turbidity resulting from aquaculture farm activities might not favor the growth of large eukaryotes but picoeukaryotes did overcome the limitation and dominate the ecological niche.

    Despite that Gamak Bay has less salinity gradient than that in the estuarine-coastal continuum like the Seomjin River to Gwangyang Bay region (Bibi et al. 2020), salinity also played a major role in defining the spatial distribution of cyanobacteria in Gamak Bay. Cyanobacteria have fairly high resistance to salinity and can adapt to hypersaline environments (Blumwald and Tel-Or 1982;Vonshak et al. 1988). In the Gwangyang Bay waters, which are adjacent to our study region, the enhanced ecological role of diverse Synechococcus is expected (Kim et al. 2018), and PE cyanobacteria, which are typically confined to high salinity waters, can potentially expand their distribution within Gwangyang Bay (Xia et al. 2020). Thus, the potential adaptation to a wide range of salinity might enable PC and PE cyanobacteria to be widespread in Gamak Bay. It is necessary to examine the ecological role of PC and PE cyanobacteria in Gamak Bay, particularly related to food consumption in shellfish of the Gamak Bay aquaculture farms.

    Long-term trends of chl-a in Gamak Bay had gradually declined until 2016 and in recent years, the chl-a level has remained low (Fig. 7). Because the role of small phytoplankton has been neglected in the coastal phytoplankton studies in South Korea, and the spatial and temporal distribution of diatoms and dinoflagellates were mainly examined in the study region (Oh et al. 2008a;Oh et al. 2009), which phytoplankton group or size was a principle player for the biomass declining is uncertain. Gamak Bay is responsible for 30% of domestic oyster production in South Korea (Kim et al. 2009) but a water temperature increase and reduction in nutrients led to exacerbating habitats and mass mortality of Pacific oysters (Kim et al. 2013). Coastal warming has been more problematic than open ocean because the shallow depth enables coastal waters to experience a rapid warming (Min and Kim 2006), and a shift to small phytoplankton with coastal warming is on-going (Kang et al. 2019;Šolić et al. 2020). Recently, the oyster production from Gamak Bay was significantly reduced due to the low growth of Pacific oysters (personal communication). Given the dominance of picoplankton or nanoplankton in the bay, the reduction in oyster production of Gamak Bay was likely related to diminishing food supply to oysters. Coastal warming and food supply to shellfish in aquaculture farm have not been intensively examined in the study region. Although picoplankton is relatively important in oligotrophic open oceans (Raven 1998) and our data only present the spring data, picoplankton existed throughout the year and acted a critical role to control the trophic interactions in Gwangyang Bay, adjacent to Gamak Bay (Kang and Oh 2021). Our data also showed the potential role of small phytoplankton as they were abundant in the bay. Therefore, our study stimulates an investigation on the role of size-fractionated phytoplankton in the food webs and fisheries in Gamak Bay, and particularly, we urge that picoplankton should be considered in a coastal water monitoring.


    This research was performed using R/V Chungkyung at Chonnam National University. We are grateful to the students and researchers who participated in this study for their assistance with field sampling and laboratory analyses. This research was supported by the “Survey of coastal fisheries resources and marine environmental ecology in the South Sea (R2022037)” funded by the National Institute of Fisheries Science (NIFS), Korea, and was financially supported by Chonnam National University (202233880001). This research as a part of the project titled “Research center for fishery resource management based on the information and communication technology” (2022, 20180384), funded by the Ministry of Oceans and Fisheries.



    Map showing sampling stations in Gamak Bay in the southern coastal waters of Korea. The crosshatch pattern and diagonal hatch pattern indicate long -lined oyster and mussel farms and fish cages, respectively.


    Physical variables of the surface and bottom waters of Gamak Bay in May 2021. (A) temperature (°C) of the surface water, (B) salinity of the surface water, (C) dissolved oxygen (DO) (mg L-1) of the surface water, (D) pH of the surface water, (E) photic depth (m), (F) temperature (°C) of the bottom water, (G) salinity of the bottom water, (H) DO (mg L-1) of the bottom water, and (I) pH of the bottom water.


    Spatial variation in dissolved inorganic nutrients in the surface and bottom waters of Gamak Bay in May 2021. (A) NO3- content (μm) in the surface water, (B) NH4+ content (μm) in the surface water, (C) PO43- content (μm) in the surface water, (D) Si(OH)4 content (μm) in the surface water, (E) NO3- content (μm) in the bottom water, (F) NH4+ content (μm) in the bottom water, (G) PO43- content (μm) in the bottom water, and (H) Si(OH)4 content (μm) in the bottom water.


    Spatial distribution of phytoplankton abundance (cells mL-1) quantified by flow cytometry. (A, F) eukaryotes>2 μm in the surface and bottom waters, (B, G) picoeukaryotes in the surface and bottom waters, (C, H) phycocyanin-containing (PC) cyanobacteria in the surface and bottom waters, (D, I) phycoerythrin -containing (PE) cyanobacteria in the surface and bottom waters, and (E, J) cryptophytes in the surface and bottom waters.


    Redundancy analysis (RDA), presenting the relationship between the major phytoplankton groups and environmental variables in Gamak Bay in May 2021. While the surface and bottom layers were clearly separated by salinity, water masses were characterized by different environmental variables.


    Correlation analysis showing the correlation coefficients between phytoplankton and environmental variables during the study period.


    Long-term variation in chlorophyll -a (chl -a) in Gamak Bay from 2011 to 2021. Chl -a concentrations have declined gradually and remained relatively stable since 2016. The lines are the results from generalized additive models showing the long-term variations in surface and bottom chl -a concentrations.


    Spatial distribution of size-fractionated chl-a in the surface waters of Gamak Bay in May 2021. (A) Total chl-a, (B) chl-a of small phytoplankton (<10 μm), (C) chl -a of nanoplankton, and (D) chl -a of microplankton.


    Results of ‘Bio -env’ analysis showing correlations of the most influential variables. Salinity, dissolved oxygen (DO), NH4+, PO43-, and Si(OH)4 were the variables most significantly correlated to the Gamak Bay ecosystem (correlation of 0.2087)

    Density of three water masses and that of surface and bottom waters. The Kruskal-Wallis test was performed with Dunn’s test. The density between the surface and bottom waters was significantly different (p<0.05). The density between the inner bay and outer bay was significantly different (p<0.05), whereas the density between the inner bay and Yeosu Harbor (p>0.05) and the density between the outer bay and Yeosu Harbor were not significantly different (p>0.05)


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    Vol. 40 No. 4 (2022.12)

    Journal Abbreviation 'Korean J. Environ. Biol.'
    Frequency quarterly
    Doi Prefix 10.11626/KJEB.
    Year of Launching 1983
    Publisher Korean Society of Environmental Biology
    Indexed/Tracked/Covered By

    Contact info

    Any inquiries concerning Journal (all manuscripts, reviews, and notes) should be addressed to the managing editor of the Korean Society of Environmental Biology. Yongeun Kim,
    Korea University, Seoul 02841, Korea.
    E-mail: /
    Tel: +82-2-3290-3496 / +82-10-9516-1611