Variation in Phytoplankton Community Due to an Autumn Typhoon and Winter Water Turbulence in Southern Korean Coastal Waters

sustainability

Article Variation in Phytoplankton Community Due to an Autumn Typhoon and Winter Water Turbulence in Southern Korean Coastal Waters

Seung Ho Baek 1,2,* , Minji Lee 1, Bum Soo Park 3 and Young Kyun Lim 1,2 1 Risk Assessment Research Center, KIOST (Korea Institute of Ocean Science and Technology), Geoje 53201, Korea; [email protected] (M.L.); [email protected] (Y.K.L.) 2 Department of Ocean Science, UST (University of Science and Technology), Daejeon 34113, Korea 3 Marine Ecosystem Research Center, KIOST (Korea Institute of Ocean Science and Technology), Busan 49111, Korea; [email protected] * Correspondence: [email protected]

Received: 26 February 2020; Accepted: 30 March 2020; Published: 1 April 2020

Abstract: Weevaluated changes in the phytoplankton community in Korean coastal waters during October 2016 and February 2017. Typhoon Chaba introduced a large amount of freshwater into the coastal areas during autumn 2016, and there was a significant negative relationship between salinity and nutrients in the Nakdong estuarine area, particularly in the northeastern area (Zone III; p < 0.001). The abundance of diatom species, mainly Chaetoceros spp., increased after this nutrient loading, whereas Cryptomonas spp. appeared as opportunists when there was relatively low diatom biomass. During winter, biotic and abiotic factors did not differ among the surface, middle, and lower layers (p > 0.01; ANOVA), implying that water mixing by winter windstorms and low surface temperature (due to the sinking of high-density water) physically accelerated mixing of the whole water column. Diatoms predominated under these conditions. Among diatoms, the centric diatom Eucampia zodiacus remained at high density at the inshore area and its abundance had a negative correlation with water temperature, implying that this species can grow at cold temperatures. On the other hand, the harmful freshwater diatom Stephanodiscus hantzschii mainly appeared in conditions with low salinity and high nutrients, implying that it can persist even in the saltwater conditions of the Nakdong Estuary. Our results indicate that hydro-oceanographic characteristics, such as river discharge after an autumn typhoon and winter water turbulence, have major effects on the composition of phytoplankton communities and can potentially affect the occurrence and characteristics of harmful algal blooms in southern Korean coastal waters.

Keywords: phytoplankton community; autumn typhoon; winter water turbulence; Korean coastal waters; harmful algal blooms

1. Introduction The seasonal phytoplankton bloom cycle and species succession that occur in temperate seas are influenced by environmental bottom-up control factors, such as temperature, light availability, and nutrient loading, and by top-down control factors, such as zooplankton grazing [1,2]. The supply of limiting nutrients has a major impact on phytoplankton composition and abundance, and this depends on species-specific differences in nutrient uptake. In addition, the growth of phytoplankton is often limited by one or more essential nutrients [3–5]. Overall, phytoplankton blooms can be partially explained by the distribution of nutrients within the water mass [6]. In particular, a sudden supply of nutrients from river discharge after rainfall and nutrient supplementation of euphotic layers by upwelling and turbulence caused by episodic wind typically promote phytoplankton growth in coastal environments [7–9].

Sustainability 2020, 12, 2781; doi:10.3390/su12072781 www.mdpi.com/journal/sustainability Sustainability 2020, 12, 2781 2 of 21

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In coastalpartially regions, explained nearby by typhoonsthe distribution often of nutrients cause physical within the disturbances, water mass [6]. suchIn particular, as upwelling, a sudden vertical mixing, terrestrialsupply of runo nutrientsff, and from sediment river discharge resuspension, after rainfall and and nutrientthese supply supplementation nutrients of euphoticdirectly to the euphotic layerlayers [10 by,11 upwelling]. In addition, and turbulence during caused winter by months, episodic windstorms wind typically can promote cause phytoplankton significant water growth in coastal environments [7–9]. mixing and increase nutrient loading in the whole water column, which is continuously accumulated In coastal regions, nearby typhoons often cause physical disturbances, such as upwelling, to the euphoticvertical layer mixing, due terrestrial to the reduced runoff, and phytoplankton sediment resuspension, nutrient and uptake these supply caused nutrients by the directly lower to levels of light and shorterthe euphotic photoperiods layer [10,11]. during In addition, winter during in winter temperate months, coastal windstorms waters. can cause Therefore, significant the water increased nutrient loadingmixing caused and increase by suchnutrient events loading significantly in the whole water aff ectscolumn, the which population is continuously dynamics accumulated and coastal to the euphotic layer due to the reduced phytoplankton nutrient uptake caused by the lower levels distribution patterns of phytoplankton [12,13]. of light and shorter photoperiods during winter in temperate coastal waters. Therefore, the increased In the coastalnutrient watersloading ofcaused the by southern such events part significantly of Korea affects (such the as Tongyeong,population dynamics Geoje, and and coastal Busan), the physical waterdistribution mass during patterns the of autumnphytoplankton and winter[12,13]. seasons is more strongly affected by the Jeju Warm Current and TsushimaIn the coastal Warm waters Current of the (TWC), southern both part ofof whichKorea (such originate as Tongyeong, from the Geoje, Kuroshio and Busan), Warm the Current physical water mass during the autumn and winter seasons is more strongly affected by the Jeju (Figure1a) [ 14–16]. The Tongyeong, Geoje, and Busan coastal areas, which are near the Kahwa Estuary and Warm Current and Tsushima Warm Current (TWC), both of which originate from the Kuroshio the NakdongWarm River Current Estuary, (Figure are located1a) [14–16]. along The theTongyeong, southeastern Geoje, and Korean Busan Peninsula coastal areas, (Figure which1 areb). near Numerous bivalve mollusksthe Kahwa (oysters Estuary and ark and shells) the Nakdong and fish River farms Estuary, have are been located established along the in southeastern these coastal Korean areas. These aquaculture activitiesPeninsula (Figure have led 1b). to Numerous the accumulation bivalve mollusks of organic (oysters matter and ark in shells) sediments, and fish and farms this have organic been matter established in these coastal areas. These aquaculture activities have led to the accumulation of organic is one of the major non-point nutrient sources in these coastal waters. The Nakdong River is Korea’s matter in sediments, and this organic matter is one of the major non-point nutrient sources in these longest rivercoastal and the waters. Nakdong The Nakdong Estuary River is Korea’s is Korea’s second longest largest river and estuary the Nakdong [17]. In Estuary addition, is Korea’s the Nakdong Estuary neighborssecond thelargest city estuary of Busan, [17]. In which addition, has the a Nakdong population Estuary ofabout neighbors 4 million. the city of Busan, which has a population of about 4 million.

Figure 1. (a) Location of the Korean Peninsula and water masses that affect the South Korean coast.2 (b) Track of Typhoon Chaba, with blue symbols indicating the range affecting the study area. (c) Sampling stations and the zones of each survey. Each survey was divided into three zones based on cluster analysis of the phytoplankton community (see Figure 9). Each zone was displayed in red (Zone I), green (Zone II), and blue (Zone III). The dotted line represents the groups of the October survey and the filled line represents the groups of the February survey. A vertical survey was conducted in the red marked stations (see Figure 4 and Figure 6). TWC, Tsushima Warm Current; TWWC, Taiwan Warm Water Current; CDW, Changjiang-Diluted Water. Sustainability 2020, 12, 2781 3 of 21

Seasonal monsoons strongly affect the climate of the Korean Peninsula. Relatively weak south to southeasterly winds and heavy precipitation are typical during the summer monsoon, and increased discharge from the Nakdong River, caused by summer-autumn typhoons, significantly affects the Busan and Geoje coastal waters. On the other hand, strong north to northwesterly winds and minimal precipitation are common during winter. The present study examined the effect of Typhoon Chaba (autumn 2016) and the subsequent winter water turbulence (winter 2017) on southern Korean coastal waters and the responses of the phytoplankton communities in this region. In particular, we examined the effects of these two major events on changes in multiple environmental variables, phytoplankton abundance, and phytoplankton composition.

2. Materials and Methods

2.1. Field Sampling and Analysis The present study was a field survey performed during autumn (18 to 19 October 2016) and winter (6 to 7 February 2017) on board the R/V Jangmok I. A total of 47 stations along the Busan, Geoje, and Tongyeong coast were established on the inner and outer coastal waters, including two major rivers—the Kahwa and Nakdong Estuaries (Figure1b). Water samples at all of the stations were collected from the surface and bottom layers using a 5-L polyvinyl chloride Niskin sampler (General Oceanics, Miami, FL, USA). In addition, samples were collected from selected middle and bottom layers to analyze abiotic and biotic factors in 18 red stations. At all of the stations, the vertical profiles of temperature, salinity, and chlorophyll-a (Chl-a) fluorescence were measured in situ using a conductivity-temperature-depth (CTD) sensor (Ocean Seven 319, Idronaut Co., Brugherio, Italy); pH and dissolved oxygen (DO) were measured using a sonde (YSI 6600, YSI Inc., Marion, MA, USA). To analyze the concentrations of inorganic nutrients, 0.5 L of each water sample was immediately passed through a 47 mm diameter glass fiber filter (GF/F); Whatman, Middlesex, U.K.) and placed in acid-cleaned polyethylene bottles, followed by the addition of HgCl2. The filtered seawater was stored at 20 C in the dark until laboratory analysis. Ammonia, nitrate, nitrite, phosphate, and − ◦ silicate concentrations were determined using a flow injection autoanalyzer (QuikChem 8000; Lachat Instruments, Loveland, CO, USA). All nutrient concentrations were calibrated using standard brine solutions (CSK Standard Solutions; Wako Pure Chemical Industries, Osaka, Japan). For the analysis of phytoplankton composition, seawater (0.5 L) was sampled and fixed with 0.5% Lugol’s solution at all of the stations. The fixed samples were concentrated to approximately 50 mL by decanting the supernatant. A Sedgewick Rafter counting chamber and a light microscope (Carl Zeiss; 37081 Gottingen, Germany) were then used to identify and quantify phytoplankton at 100 , 200 , and 400 . × × × Measures of the phytoplankton diversity for each sample were based on the number of species in samples of the surface, middle, and bottom layers using the Shannon-Weaver diversity index (H0) and Pielou’s evenness index (J0), a measure of the similarity in the numbers of different species:

XS H0 = Pi(In Pi) (1) − i = 1

H0 J0 = (2) ln(s) where Pi is the total number of individuals in a species, and S in the total number of species. In addition, Margalef’s richness index (d), which accounts for sampling bias, was calculated as

(S 1) d0 = − (3) ln(n) where n is the total number of individuals in the sample. Sustainability 2020, 12, 2781 4 of 21

The contour maps were drawn using the software Surfer Version 12 (Golden Software LLC, Golden, CO, USA).

2.2. Statistical Analysis For phytoplankton community analyses, cluster analysis (group average) and non-metric multidimensional scaling (MDS) ordination (based on the Bray-Curtis similarity index) were used for the analysis of species abundance data using SPSS version 17.0 (SPSS Inc., Chicago, IL, USA) [18]. Differences were considered significant when the p value was below 0.05. The differences in abiotic and biotic factors (including phytoplankton abundance) between three geographical zones (see below) were assessed by one-way analysis of variance (ANOVA) with Tukey’s test, and the differences between surface and bottom layers were evaluated by the t-test using SPSS version 17.0. Canonical correspondence analysis (CCA) can explain the relationships between a community and the environment. The impact of the measured environmental factors on the occurrences of dominant phytoplankton at the genus and species levels was investigated by CCA using CANOCO for Windows 4.5 software. The measured environmental factors, including temperature, salinity, ammonia, nitrate + nitrite, phosphate, and silicate, were the explanatory variables. Ln(x+1) transformation was performed on the phytoplankton data for CCA ordination. In addition, principal component analysis (PCA) was performed for season-averaged abiotic and biotic factors to identify environmental factors that were related to phytoplankton dynamics. Statistical computations for these procedures were performed using XLSTAT 2010 (AddionSoft TM).

3. Results

3.1. Precipitation and River Discharge Figure2 shows the daily precipitation and river discharge data for the Nakdong River from 2014 to 2018. The amounts of both were generally high during summers and low during winters. From summer 2016 to spring 2017, the daily river discharge and precipitation were signiﬁcantly increased during autumn and remained low until the spring of the next year. In particular, there was a large amount of rainfall and river discharge after the passage of Typhoon Chaba on 5 October (Figure2b). The two-week accumulated river discharge and precipitation prior to the sample date were greater during autumn (October) than the other three seasons, and these remained low during winter (February). Sustainability 2020, 12, 2781 5 of 21 Sustainability 2019, 11, x FOR PEER REVIEW 5 of 21

Figure 2. (a)(a) Precipitation atat BusanBusan and and the the river river discharge discharge of of the the Nakdong Nakdong River River from from 2014 2014 to 2018.to 2018. (b) (b)Precipitation Precipitation and and river river discharge discharge from from June June 2016 2016 to Mayto May 2017. 2017. (c )(c) Precipitation Precipitation and and river river discharge discharge 3 weeks3 weeks before before and and 1 week 1 week after after the Octoberthe October survey. survey. (d) Precipitation (d) Precipitation and river and dischargeriver discharge 3 weeks 3 weeks before beforeand 1 weekand 1 after week the after February the February survey. survey. Dotted Dotted lines in lines c and in d c indicateand d indicate sampling sampling dates. dates. 3.2. Horizontal and Vertical Distributions of Environmental Factors 3.2. Horizontal and Vertical Distributions of Environmental Factors The horizontal surface seawater temperature (SST) at the 47 stations varied during October, from The horizontal surface seawater temperature (SST) at the 47 stations varied during October, from 19.2 C (Zone II) to 22.0 C (Zone I) (Figure3a). The average SST of Zone II was more than two degrees 19.2 ◦°C (Zone II) to 22.0 °C◦ (Zone I) (Figure 3a). The average SST of Zone II was more than two degrees lower than that of the other zones. In contrast, the SST also varied greatly during February, ranging lower than that of the other zones. In contrast, the SST also varied greatly during February, ranging from 7.7 C (inshore of Zone I) to 14.1 C (Zone III) (Figure3b). The water temperature at the outer from 7.7 ◦°C (inshore of Zone I) to 14.1 ◦°C (Zone III) (Figure 3b). The water temperature at the outer coastal region (Stations 12, 13, 14, 15, and 16) in Zone I were relatively high (~14 C), and formed a coastal region (Stations 12, 13, 14, 15, and 16) in Zone I were relatively high (~14°C),◦ and formed a strong temperature front between the outer and inner coastal regions in Zone I (Figure3b). Salinity in strong temperature front between the outer and inner coastal regions in Zone I (Figure 3b). Salinity the surface layer decreased dramatically after rainfall during October, and the lowest salinity was 26.8; in the surface layer decreased dramatically after rainfall during October, and the lowest salinity was however, salinity during February also exhibited a small spatial variation in the surface layer, varying 26.8; however, salinity during February also exhibited a small spatial variation in the surface layer, from 30.4 to 32.7, excluding Station 37 of the Nakdong Estuaries (Figure3c). The pH did not vary varying from 30.4 to 32.7, excluding Station 37 of the Nakdong Estuaries (Figure 3c). The pH did not greatly during October and February. The pH during October gradually increased from 7.8 to 8.0 from vary greatly during October and February. The pH during October gradually increased from 7.8 to the inner to the outer coastal regions (Figure2e,f). In contrast, pH during February was relatively low 8.0 from the inner to the outer coastal regions (Figure 2e,f). In contrast, pH during February was in the outer coastal region of Zone I (Figure3e,f). DO was greater during February than October and relatively low in the outer coastal region of Zone I (Figure 3e,f). DO was greater during February than ranged from 6.3 to 8.3 mg L 1 during October and 8.2 to 10.3 mg L 1 during February (Figure3g,h). October and ranged from 6.3− to 8.3 mg L-1 during October and 8.2− to 10.3 mg L-1 during February (Figure 3g,h).

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FigureFigure 3. 3.Horizontal Horizontal distributionsdistributions ofof temperaturetemperature during during ( a(a)) October October andand ( (b)b) February,February, salinity salinity during during (c(c)) October October and and (d )(d) February, February, pH pH during during (e) October (e) October and ( fand) February, (f) February, and dissolved and dissolved oxygen oxygen (DO) during (DO) (gduring) October (g) October and (h) February.and (h) February. The contour The contour maps were maps drawn were usingdrawn the using software the software Surfer VersionSurfer Version 12. 12. Analysis of the vertical profiles indicated that water temperature during October had a strong verticalAnalysis stratification of the invertical Zone III,profiles except indicated at Station that 37, andwater was temperature gradually stratified during October in Zone had II (Figure a strong4). Thevertical water stratification temperature in Zone during III, October except at ranged Station from 37, and 15.4 was to 22.2gradually◦C. During stratified February, in Zone the II (Figure water column4). The waswater well temperature mixed vertically, during and October the temperature ranged from diff erences15.4 to 22.2°C. among theDuring three February, zones were the greater water (Figurecolumn4b). was During well mixed October, vertically, there was and high the salinity temperature from the differences bottom to among the surface, the three but the zones salinity were wasgreater relatively (Figure low 4b). in During the upper October, layer (0there to 3 was m) at high Station salinity 37 of from Zone the III, bottom near the to Nakdongthe surface, Estuaries but the (Figuresalinity4 c).was In relatively contrast tolow October, in the high-salinityupper layer (0 water to 3 m) ( > at32) Station was common 37 of Zone at Zone III, near II and the III Nakdong during FebruaryEstuaries (Figures (Figure 34c).d and In 4contrastd). to October, high-salinity water (> 32) was common at Zone II and III during February (Figures 3d and 4d).

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Figure 4. Vertical profiles of temperature during (a) October and (b) February, salinity during (c) Figure 4. Vertical profiles of temperature during (a) October and (b) February, salinity during (c) October and (d) February, and chlorophyll-a (Chl-a) during (e) October and (f) February along the red October and (d) February, and chlorophyll-a (Chl-a) during (e) October and (f) February along the red stations shown in Figure1c. The contour maps were drawn using the software Surfer Version 12. stations shown in Figure 1c. The contour maps were drawn using the software Surfer Version 12. There were large spatial variations in dissolved inorganic nutrients (Figure5). During October, the There were large spatial variations in dissolved inorganic nutrients (Figure 5). During October, nitrate + nitrite concentration varied from 0.64 to 37.4 µM; it was higher in and around the Nakdong the nitrate + nitrite concentration varied from 0.64 to 37.4 µM; it was higher in and around the Estuaries of Zone III and lower in the inner regions of Zone I. There were also variations in the Nakdong Estuaries of Zone III and lower in the inner regions of Zone I. There were also variations in concentrations of ammonia (0.04 to 5.1 µM), phosphate (0.1 to 1.0 µM), and silicate (1.5 to 54.3 µM), with the concentrations of ammonia (0.04 to 5.1 µM), phosphate (0.1 to 1.0 µM), and silicate (1.5 to 54.3 higher levels at the inner regions than the outer coastal regions. The ranges of nutrient concentrations µM), with higher levels at the inner regions than the outer coastal regions. The ranges of nutrient during October were similar to those during February, but the horizontal distribution patterns were concentrations during October were similar to those during February, but the horizontal distribution different, and the concentrations were high at most stations during February. In particular, there were patterns were different, and the concentrations were high at most stations during February. In high nutrient levels during both seasons in the mouths of the Nakdong Estuaries, where freshwater particular, there were high nutrient levels during both seasons in the mouths of the Nakdong was discharged. In the vertical profile of October, the nitrogen concentration was constant vertically Estuaries, where freshwater was discharged. In the vertical profile of October, the nitrogen but was low at the surface layer of several stations. concentration was constant vertically but was low at the surface layer of several stations.

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FigureFigure 5.5. Horizontal distributions distributions of of inorganic inorganic nutrients; nutrients; nitrate nitrate + +nitritenitrite during during (a) (aOctober) October and and (b) (February,b) February, ammonium ammonium during during (c) ( cOctober) October and and (d) (d )February, February, phosphate phosphate during during (e) (e) October and ((f)f) February,February, andand silicatesilicate duringduring ((g)g) OctoberOctober andand ((h)h) February.February. TheThe contourcontour mapsmaps werewere drawndrawn usingusing thethe softwaresoftware SurferSurfer VersionVersion 12. 12.

AnalysisAnalysis ofof thethe verticalvertical distributionsdistributions ofof nutrientsnutrients indicatedindicated thatthat phosphatephosphate andand silicatesilicate werewere higherhigher inin thethe bottombottom layerslayers (Figure(Figure6 6).). During During February, February, these these high high nutrient nutrient levels levels in in the the whole whole water water columncolumn werewere maintained, maintained, except except at at Zone Zone I. I. Among Among these these nutrients, nutrients, the the nitrogen nitrogen was was high high at Stationat Station 37 (Nakdong37 (Nakdong Estuaries) Estuaries) and theand nitrogen the nitrogen and silicate and silicate concentrations concentrations were low were at thelow inner at the coastal inner regionscoastal ofregions Zone I.of Zone I. The average Shannon‒Weaver (H′) diversity of phytoplankton was 2.29 during October, and the greatest H′ was 2.40 at Zone III. The average evenness (J′) was 0.74, and the greatest J′ was 0.83 at Zone II. During February, the average H′ was 2.30 and the average J′ was 0.78, and both of these parameters were greatest at Zone II.

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Figure 6. Vertical profiles of inorganic nutrients along the red stations shown in Figure1c. Nitrogen Figure 6. Vertical profiles of inorganic nutrients along the red stations shown in Figure 1c. Nitrogen (Nitrate + Nitrite + Ammonium) during (a) October and (b) February, phosphate during (c) October (Nitrate + Nitrite + Ammonium) during (a) October and (b) February, phosphate during (c) October and (d) February, and silicate during (e) October and (f) February. The contour maps were drawn using and (d) February, and silicate during (e) October and (f) February. The contour maps were drawn the software Surfer Version 12. using the software Surfer Version 12. The average Shannon-Weaver (H ) diversity of phytoplankton was 2.29 during October, and the 3.3. Chl-a Concentration and Presence of 0Different Phytoplankton greatest H0 was 2.40 at Zone III. The average evenness (J0) was 0.74, and the greatest J0 was 0.83 at Zone II. DuringFigure February, 7 shows the the average spatial Hand0 was temporal 2.30 and variations the average of JChl-0 wasa concentration 0.78, and both and of these phytoplankton parameters wereabundance. greatest The at Zonesurface II. Chl-a concentration varied from 0.3 to 3.1 µg L–1 during October and from 0.6 to 6.13 µg L–1 during February. The Chl-a concentration was about 2-fold higher during February 3.3.than Chl-a October. Concentration In particular, and Presence the Chl- ofa Diconcentrationfferent Phytoplankton in October was high in outer coastal water but was Figurehigh at7 theshows inner the coastal spatial regions and temporal during variationsFebruary. ofThe Chl- totala concentrationphytoplankton and abundance phytoplankton varied abundance.from 3.7 × 10 The4 to surface8.4 × 10 Chl-5 cellsa concentrationL–1 (mean ± SD: varied 3.3 × from105 cells 0.3 toL–1 3.1 ± 2.4µg × L –1105during) in October October (Figure and from 6c) and 0.6 tofrom 6.13 3.8µg ×L 10–14during to 7.1 × February. 105 cells L The–1 (mean Chl-a concentration± SD: 2.1 × 105 was cells about L–1 ± 2-fold2.0 × 10 higher5) during during February February (Figure than October.5c). During In particular,October and the February, Chl-a concentration diatoms accounted in October for was more high of in the outer total coastal phytoplankton water but wasbiomass high at theall innerof the coastal stations, regions and duringthe levels February. of dinoflagellates The total phytoplankton and cryptophytes abundance were varied relatively from low. 3.7 The104 × todistribution 8.4 105 cells of cryptophytes L–1 (mean differedSD: 3.3 during105 cells October L–1 and2.4 February.105) in October (Figure6c) and from 3.8 × ± × ± × 104 to 7.1 105 cells L–1 (mean SD: 2.1 105 cells L–1 2.0 105) during February (Figure5c). × × ± × ± × During October and February, diatoms accounted for more of the total phytoplankton biomass at all of the stations, and the levels of dinoflagellates and cryptophytes were relatively low. The distribution of cryptophytes differed during October and February.

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FigureFigure 7.7. HorizontalHorizontal distributionsdistributions ofof Chl-Chl-aa duringduring ((a)a) OctoberOctober andand ((b)b) February,February, totaltotal phytoplanktonphytoplankton abundanceabundance during during (c ) (c) October October and and (d) February, (d) February, and speciﬁc and specific horizontal horizontal phytoplankton phytoplankton classes (diatoms, classes dinoﬂagellates,(diatoms, dinoflagellates, cryptophytes, cryptophytes, and others) and during others) (e) October during and(e) October (f) February. and (f) The February. contour The maps contour were drawnmaps were using drawn the software using the Surfer software Version Surfer 12. Version 12.

WeanalyzedWe analyzed the the abundances abundances of theof the dominant dominant phytoplankton phytoplankton genera, genera, which which were were composed composed of eight of diatomseight diatoms and one and cryptophyte one cryptophyte (Figure8 ). (Figure During 8). October, During the October, diatom theChaetoceros diatom spp.Chaetoceros was dominant spp. was at Zonesdominant I and at II, Zones and Pseudo-nitzschia I and II, and Pseudo-nitzschiaspp. had a high spp. abundance had a high at Zone abundance II. The secondat Zone most II. The dominant second groupsmost dominant were the diatomsgroups wereEucampia the diatomsspp., Skeletonema Eucampiaspp., spp.,Pseudo-nitzschia Skeletonema spp.,spp., Pseudo-nitzschia and Asterionella spp., glacialis and. DuringAsterionella February, glacialisE.. zodiacus During February,was present E. at zodiacus the inshore was present area of at Zone the I, inshore and Skeletonema area of Zonespp. I, and and ChaetocerosSkeletonemaspp. spp. had and relatively Chaetoceros high spp. levels had at Zonerelatively III. Overall, high levels the freshwater at Zone III. diatom Overall,Stephanodiscus the freshwaterspp. haddiatom a high Stephanodiscus abundance atspp. Station had a 37 high (Nakdong abundance Estuaries at Station of Zone 37 (Nakdong III). The abundance Estuaries of cryptophytesZone III). The wasabundance relatively of highcryptophytes at Zone III was during relatively October high and at at Zone Zone IIII during during February. October and at Zone I during February.

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FigureFigure 8. 8. HorizontalHorizontal distributions distributions of the 1 ofst dominant the 1st dominant genus in each genus station in during each (a) station October during and (b) ( a) October and nd (February,b) February, and the and 2 the dominant 2nd dominant genus in each genus station in during each station(c) October during and (d) (c February.) October Each and zone, (d) February. Each separated by multidimensional scaling (MDS), is marked with a colored line; Red: zone I, Yellow: zone, separated by multidimensional scaling (MDS), is marked with a colored line; Red: zone I, Yellow: zone II, and Blue: zone III. Cluster analysis of the phytoplankton community. The contour maps were zonedrawn II, using and the Blue: software zone Surfer III. Cluster Version analysis 12. of the phytoplankton community. The contour maps were drawn using the software Surfer Version 12. We performed cluster analysis and non-metric MDS to assess the phytoplankton composition and Weto quantify performed phytoplankton cluster analysisduring each and month non-metric (Figure 9 MDS and Figure to assess 1c). The the results phytoplankton indicated composition andthat tothe quantify phytoplankton phytoplankton community structure during separated each month into three (Figures distinct9 and groups1c). during The results October indicated and that the phytoplanktonFebruary (p < 0.001). community During October, structure the separated phytoplankton into threecommunity distinct structure groups at duringZone II was October the and February most distinct in that it had a 50% similarity to the other clusters. All of the stations at Zone I were (plocated< 0.001). at the During inner and October, outer coastal thephytoplankton regions of Tongyeong, community and moststructure stations that at branched Zone II at was Zone the most distinct inII thatwere itin had the semi-closed a 50% similarity area of the to western the other parts clusters. of Geoje AllIsland. of theAll stations stations at atZone Zone III were I were in located at the innerthe inner and and outer outer coastal coastal regionswaters of of the Tongyeong, Nakdong Estuaries. and most During stations February, that Zone branched I (located at at Zone the II were in the semi-closedinner coastal water area ofof Tongyeong) the western had parts a 61% of similarity Geoje Island. to the other All clusters. stations Most atZone stations III at were Zone inII the inner and outerwere coastalin the outer waters coastal of theregions Nakdong of the southern Estuaries. area Duringof Geoje February,Island, which Zone branched I (located at 58%. at In the inner coastal addition, Zone III (located at the inshore and offshore regions of Nakdong Estuaries) branched at water38%. Overall, of Tongyeong) this analysis had indicated a 61% similarity that the geographical to the other characteristics clusters. Most of phytoplankton stations at Zonewere II were in the outersimilar coastal during regionsOctober and of the February. southern area of Geoje Island, which branched at 58%. In addition, Zone III (located at the inshore and oﬀshore regions of Nakdong Estuaries) branched at 38%. Overall, this analysis indicated that the geographical characteristics of phytoplankton were similar during October and February.

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Figure 9. (a)Cluster Cluster analysis analysis of of phytoplankton phytoplankton assemblages assemblages during during ( a(a)) October October andand ((b)b) February,February, andand multidimensional scaling (MDS) analysis during ((c)c) OctoberOctober andand ((d)d) FebruaryFebruary (see(see FigureFigure1 1c.).c.).

3.4. PCA and CCA of Factors Affecting Affecting Phytoplankton We performedperformed PCA toto identifyidentify environmentalenvironmental factors that aaffectedffected phytoplanktonphytoplankton dynamics during both sampling sampling periods periods (Table (Table 1).1). During During October, October, Principal Principal Component Component 1(PC1) 1(PC1) accounted accounted for for 44.5% of the variance, and there were large positive loadings for NO + NO , NH , silicate, 44.5% of the variance, and there were large positive loadings for NO2 + 2 NO3, NH3 4, silicate,4 and andphosphate phosphate and large and large negative negative loadings loadings for Chl for-a Chl, pH,-a, DO, pH, DO,total totalphytoplankton, phytoplankton, and diatoms. and diatoms. PC2 PC2accounted accounted for 24.1% for 24.1% of the of the variance, variance, and and only only had had large large negative negative loadings loadings for for dinoflagellates,dinoflagellates, cryptophytes, and other species. During February, PC1 accounted for 47.5% of the variance and there were strong negativenegative loadings for waterwater temperature,temperature, phosphate, and silicate, and strong positive loadings for for Chl Chl-a-a, pH,, pH, DO, DO, total total phytoplankton, phytoplankton, diatoms, diatoms, and and cryptophytes. cryptophytes. PC2 accounted PC2 accounted for 24.0% for of24.0% the variance, of the variance, and there and was there a strong was negative a strong loading negative for loading salinity, for and salinity, strong positive and strong loadings positive for NO + NO , NH , silicate, and other species. loadings2 for3 NO24 + NO3, NH4, silicate, and other species.

Table 1. Loadings of the first three principal components from principal component analysis (PCA) of the surface waters during October and February. Loadings greater than 0.6 or less than −0.6 are in bold.

October February PC1 PC2 PC3 PC1 PC2 PC3 12 Sustainability 2020, 12, 2781 13 of 21

Table 1. Loadings of the ﬁrst three principal components from principal component analysis (PCA) of the surface waters during October and February. Loadings greater than 0.6 or less than 0.6 are in bold. − October February PC1 PC2 PC3 PC1 PC2 PC3

SustainabilityTemperature 2019, 11, x FOR PEER –0.56 REVIEW –0.30 –0.50 –0.85 –0.10 13 0.47 of 21 Salinity –0.32 0.73 0.45 –0.53 –0.80 0.10 NO2 + NO3 Temperature0.75 –0.59 –0.56 –0.30 –0.16 –0.50 –0.85 –0.17 –0.10 0.96 0.47 0.12 NH4 Salinity0.70 0.13 –0.32 0.73 –0.47 0.45 –0.53 0.03 –0.80 0.900.10 –0.28 PO4 0.94 0.08 –0.11 –0.88 0.24 –0.25 NO2 + NO3 0.75 –0.59 –0.16 –0.17 0.96 0.12 SiO2 0.82 –0.49 –0.17 –0.74 0.61 –0.02 NH4 0.70 0.13 –0.47 0.03 0.90 –0.28 Chl-a –0.77 –0.17 –0.42 0.86 –0.15 0.21 PO4 0.94 0.08 –0.11 –0.88 0.24 –0.25 DO –0.62 –0.51 0.03 0.93 0.10 –0.14 pH SiO–0.932 –0.170.82 –0.49 –0.02 –0.17 –0.740.78 0.61 0.01–0.02 0.40 Total phytoplankton Chl-–0.82a –0.36–0.77 –0.17 –0.23 –0.42 0.860.88 –0.15 0.17 0.21 0.28 Total diatom DO–0.84 –0.29–0.62 –0.51 –0.25 0.03 0.930.87 0.10 0.14 –0.14 0.28 Total dinoﬂagellatepH –0.14 –0.68–0.93 –0.170.48 –0.02 0.78 –0.30 0.01 –0.19 0.40 0.45 Total cryptophyteTotal phytoplankton 0.18 –0.72–0.82 –0.360.33 –0.23 0.880.73 0.17 –0.07 0.28 –0.16 OthersTotal diatom 0.56 –0.64–0.84 –0.29–0.28 –0.25 0.87 0.20 0.14 0.78 0.28 0.28 EigenvalueTotal dinoflagellate 6.61 3.62 –0.14 –0.68 1.87 0.48 –0.30 7.12 –0.19 3.59 0.45 1.53 Variability (%)Total cryptophyte 44.05 24.15 0.18 –0.72 12.45 0.33 0.73 47.47 –0.07 23.96 –0.16 10.21 Cumulative %Others 44.05 68.20 0.56 –0.64 80.65 –0.28 0.2047.47 0.78 71.430.28 81.64 Eigenvalue 6.61 3.62 1.87 7.12 3.59 1.53 Variability (%) 44.05 24.15 12.45 47.47 23.96 10.21 Our CCA analysisCumulative indicated % that there 44.05 was a 68.20 relationship 80.65 between 47.47 environmental 71.43 81.64 factors (water temperature,Our CCA salinity, analysis pH, indicated DO, and that inorganic there was nutrients) a relationship and between the dominant environmental phytoplankton factors (water groups duringtemperature, October and salinity, February pH, DO, (Figure and 10inorganic). During nutrients) October, and the the dominant dominant diatom phytoplankton species hadgroups similar abundances,during October with Chaetoceros, and February Eucampia, (Figure and10). DuringSkeletonema October,grouping the dominant together. diatom These species genera had had similar positive correlationsabundances, with withChl- aChaetoceros,, salinity, and Eucampia, temperature and Skeletonema and negative grouping correlations together. with These NO genera2 + NO had3, NH 4, phosphate,positive and correlations silicate. with During Chl-a, February,salinity, and there temperature was a positiveand negative relationship correlations of with temperature NO2 + NO3, with phosphateNH4, phosphate, and these twoand silicate. factors During had negative February, correlations there was a with positive pH andrelationship DO. The of genus temperatureEucampia withhad a positivephosphate correlation and these with two Chl factors-a, implying had negative that speciescorrelations in this with genus pH and were DO. mostlyThe genus responsible Eucampia had for the a positive correlation with Chl-a, implying that species in this genus were mostly responsible for the high Chl-a levels. In addition, the freshwater diatom Stephanodiscus spp. had a positive correlation high Chl-a levels. In addition, the freshwater diatom Stephanodiscus spp. had a positive correlation with nutrient levels, particularly nitrogen compounds. with nutrient levels, particularly nitrogen compounds.

FigureFigure 10. Canonical 10. Canonical correspondence correspondence analysis analysis ofof thethe relationships of of the the dominant dominant phytoplankton phytoplankton

generagenera and environmentaland environmental factors factors (temperature, (temperature, salinity, salinity, Chl-Chl-a,, DO, DO, pH, pH, and and inorganic inorganic nutrients nutrients (NO (NO2 2 + NO3, NH4, PO4, and Si)) during (a) October and (b) February. + NO3, NH4, PO4, and Si)) during (a) October and (b) February.

4. Discussion

4.1. Relationship of Phytoplankton Bloom With High Nutrient Level After Typhoon Chaba 13 Sustainability 2020, 12, 2781 14 of 21

4. Discussion

4.1. Relationship of Phytoplankton Bloom With High Nutrient Level After Typhoon Chaba According to path of Typhoon Chaba, phytoplankton primary production was significantly increased in Korean coastal waters during the autumn. The increased levels of nutrients in coastal waters can have a significant impact on annual primary production [11] and can determine phytoplankton community structure [19,20]. In this study, an increase in precipitation due to Typhoon Chaba during the two weeks led to an elevation of the Nakdong River discharge, resulting in nutrients loading to the nearby coastal waters; there were significant negative relationships between salinity and nutrients at Zone III (NO + NO , r = –0.92, p < 0.001; NH , r = 0.72, p < 0.001; PO , r= 0.95, p < 0.001; and SiO , 2 3 4 − 4 − 2 r = 0.94, p < 0.001), implying that the abundant nutrients from the river discharge were supplied to − Zone III and remained present even 2 weeks after the typhoon. Based on our data, there was a time lag (two weeks) between nutrient loading and the increase in phytoplankton abundance in this study. Similar to with our data, a time lag (two weeks) between two events has occasionally been observed in Korean coastal waters, even when this introduction is sudden [8].

4.2. Spatial and Temporal Variations of Biotic and Abiotic Factors There was a clear difference in phytoplankton community structure between Zone III and Zones I and II; diatom species were the common dominant taxa in all three zones, but flagellated species (dinoflagellates and cryptophytes) were predominant only in the estuarine waters (Zone III). Moreover, the species number, diversity, and richness of phytoplankton at Zone III were greater than at Zones I and II (p < 0.01; ANOVA). Interestingly, based on our measurement data, abiotic conditions at Zone III were clearly distinct, compared to the other two zones; the salinity was slightly lower at Zone III than at Zone I and Zone II (F = 8.01, p < 0.01; ANOVA; Figure 11), implying a significant influence by the Nakdong River discharge after the typhoon. In addition, the main nutrients at Zone III (nitrogen and silicate) were significantly greater there than at Zones I and II (nitrogen, F = 5.53, p < 0.01; silicate, F = 4.98, p < 0.05). Smayda [4] demonstrated that water movements and environmental factors in the water column can strongly affect the spatio-temporal distribution of phytoplankton. Therefore, together with these findings, the spatio-temporal difference of abiotic factors among the three zones might lead to variation in the phytoplankton community structure in southern Korean coastal waters. Our analysis of surface biotic and abiotic factors during the winter indicated that the water temperature of the inshore area at Zone I was significantly lower than at Zones II and III (F = 23.3, p < 0.01; ANOVA; Figure 12). This suggests that a relatively high water temperature was maintained within the Busan coastal water (Zone III) because of the introduction of a large amount of warm water from the TWC and a relatively small input of cold freshwater from the Nakdong River during the winter. Baek et al. [9] reported that the Busan coast is affected by the TWC throughout the year, and thus has relatively warm water (>10◦C) even during winter. Whereas, we found no difference in salinity between the three zones (p > 0.01; ANOVA). However, the DO and pH at Zone I were higher than at Zones II and III (DO: F = 15.51, p < 0.01; pH: F = 4.39, p < 0.05; ANOVA), which may be related to the high level of Chl-a at Zone I (Zone I vs. Zone II and Zone III; F = 8.32, p < 0.05; ANOVA). Based on previous findings, the DO concentration and pH level typically correlate with phytoplankton population density (Chl-a) and daytime photosynthesis [21,22]. Therefore, higher phytoplankton biomass at Zone I may lead to an elevation of the DO and pH levels in the winter season. Sustainability 2020, 12, 2781 15 of 21 Sustainability 2019, 11, x FOR PEER REVIEW 15 of 21

FigureFigure 11. 11.Box Box plots plots ofof environmentalenvironmental and biological factors factors in in the the surface surface waters waters of of the the three three zones zones duringduring October. October. Median: Median: solidsolid line within the the box. box. Results Results were were from from one-way one-way ANOVA, ANOVA, “a” “a” and and “b” “b” indicateindicate significant significant di differences,fferences,N.S.—not N.S.—not significant,significant, *** p<0.05,< 0.05, ** **p<0.01,p < 0.01, and and * p<0.005. * p < 0.005.

Our analysis of surface biotic and abiotic factors during the winter indicated that the water temperature of the inshore area at Zone I was significantly lower than at Zones II and III (F = 23.3, p < 0.01; ANOVA; Figure 12). This suggests that a relatively high water temperature was maintained within the Busan coastal water (Zone III) because of the introduction of a large amount of warm water from the TWC and a relatively small input of cold freshwater from the Nakdong River during the winter. Baek et al. [9] reported that the Busan coast is affected by the TWC throughout the year, and 15 Sustainability 2019, 11, x FOR PEER REVIEW 16 of 21

thus has relatively warm water (>10°C) even during winter. Whereas, we found no difference in salinity between the three zones (p > 0.01; ANOVA). However, the DO and pH at Zone I were higher than at Zones II and III (DO: F = 15.51, p < 0.01; pH: F = 4.39, p < 0.05; ANOVA), which may be related to the high level of Chl-a at Zone I (Zone I vs. Zone II and Zone III; F = 8.32, p < 0.05; ANOVA). Based on previous findings, the DO concentration and pH level typically correlate with phytoplankton Sustainabilitypopulation2020 density, 12, 2781 (Chl-a) and daytime photosynthesis [21,22]. Therefore, higher phytoplankton16 of 21 biomass at Zone I may lead to an elevation of the DO and pH levels in the winter season.

Figure 12. Box plots of environmental and biological factors in the surface waters of the three zones during February. Median: solid line within the box. Results were from one-way ANOVA. “a” and “b” indicate significant differences, N.S.—not significant, *** p < 0.05, ** p < 0.01, and * p < 0.005. 16

In contrast, the levels of nitrogen, phosphate, and silicate were significantly lower at Zone I than at Zones II and III (nitrogen: F = 4.57, p < 0.05; phosphate: F = 11.93, p < 0.01; silicate: F = 13.17, p < 0.01; ANOVA), implying a greater uptake of these nutrients by phytoplankton (especially diatoms) at Zone I. This result indicates that the high abundance of diatoms at Zone I was largely responsible for the high total phytoplankton abundance. Moreover, although the number of dinoflagellates did not differ Sustainability 2020, 12, 2781 17 of 21 among the zones (p > 0.01; ANOVA), the total abundance of phytoplankton, diatoms, and cryptophytes was greater at Zone I than at Zones II and III (p < 0.01; ANOVA). In addition, the phytoplankton diversity and evenness were lower at Zone I than at Zones II and III, implying that the phytoplankton diversity may have declined because of the dominance of the diatom E. zodiacus during the winter. E. zodiacus, a large centric diatom species, has a worldwide distribution and sometimes forms dense blooms during winter and early spring in Korean coastal waters [2], suggesting that this species can grow in cold water, as noted below.

4.3. Vertical Variations of Biotic and Abiotic Factors We analyzed the biotic and abiotic factors in the whole water column to examine the impact of vertical water mixing by the typhoon during the autumn and of wind-mediated water turbulence during the winter. During the autumn, the temperature, DO, and pH at the surface were significantly greater than at the middle and bottom layers (p < 0.001; ANOVA; Figure 13). Although salinity at the surface layer had significant variations between areas, it did not differ significantly between the surface, middle, and bottom layers (F = 2.18, p > 0.01; ANOVA). The Chl-a level also did not differ between the three layers (p > 0.01; ANOVA), although the total phytoplankton (F = 6.91), diatoms (F = 6.18), dinoflagellates (F = 4.80), and cryptophytes (F = 7.29) were more abundant in the surface layer than the middle and bottom layers (p < 0.01; ANOVA). Whereas, biotic and abiotic factors during the winter did not differ between the three layers (p > 0.01; ANOVA), implying that both water mixing by windstorm events and the low temperature of the surface layer promoted mixing of the whole water column (Figure 13b). We found that phytoplankton responded to the nutrient increase that occurred following wind-driven mixing of the water column, and there is evidence that blooms in other regions also occurred following continuous winter water mixing due to wind events in temperate coastal waters [23]. It is well known that wind affects water stability and turbulence. Our finding of high phytoplankton abundance during the winter throughout the whole water column is similar to the findings of Yin et al. [23] and Baek et al. [9]. These results thus confirm that mixing of the whole water column by winter windstorms and low surface temperature (due to the sinking of high-density water) is likely to be a key event in regulating the biotic and abiotic characteristics of the water column. In particular, a water mixing event during winter has an important role in driving diatom species from the surface layer to the middle and bottom layers. [2,24,25].

4.4. Phytoplankton Community Structure and Characteristics of Phytoplankton Species It is well known that spatial and temporal variability of environmental parameters can affect phytoplankton community structure in coastal waters. During October, our cluster analysis and MDS analysis separated the phytoplankton community into three major groups (p < 0.001). Most stations had high densities of the centric diatom Chaetoceros spp. (including C. didymus and C. lorenzianus), and this is known as a cosmopolitan species. In particular, diatom species accounted for 92.7% of the total phytoplankton species, and four diatoms (Chaetoceros spp., Skeletonema spp., E. zodiacus, and Pseudo-nitzschia spp.) accounted for approximately 84.6% of the total. The second most dominant species were Skeletonema spp. and E. zodiacus at Zone I, Pseudo-nitzschia spp. at Zone II, and small Cryptomonas spp. at Zone III. Based on our CCA, the abundances of Chaetoceros, Eucampia, and Skeletonema had positive correlations with Chl-a, implying that they were responsible for the high phytoplankton biomass during the autumn. Baek et al. [2] reported that a large introduction of nutrients following heavy precipitation in the inner area of Gwangyang Bay (Korea) led to a large bloom of diatoms (especially Skeletonema spp.) during summer. Tsuchiya et al. [19,20] demonstrated that 5 days after the passage of a summer typhoon at Sagami Bay (Japan), the dominant dinoflagellates shifted to diatom groups, such as Chaetoceros spp. and Cerataulina spp. Thus, this shows high nutrient input following typhoon-stimulated diatom blooms, as in the present study. In contrast, we found that Cryptomonas spp. (primarily flagellates) occurred in regions with a relatively low salinity and high levels of nutrients at Zone III. Our previous seasonal survey also demonstrated that Cryptomonas spp. Sustainability 2020, 12, 2781 18 of 21 were predominant (> 70%) during the autumn in the Seomjin estuarine region in Gwangyang Bay [2] and in the Nakdong Estuary [9]. As demonstrated by Klaveness [26], cryptophytes grow well in the presence of pulsed nutrient loading and tend to dominate when nutrient conditions are non-limiting. Therefore, the nutrient loading after a typhoon increases the abundances of diatom species, especially Chaetoceros spp.; in contrast, Cryptomonas spp. were opportunists that appeared when the diatom biomass was relatively low, particularly at Zone III sites. Sustainability 2019, 11, x FOR PEER REVIEW 18 of 21

FigureFigure13. 13. BoxBox plotsplots ofof abiotic and biotic factors factors at at the the surface, surface, middle, middle, and and bottom bottom layers layers of ofthe the red red stationstation during during ((a)a) OctoberOctober andand ((b)b) February. Median: Median: solid solid line line within within the the box. box. Results Results were were analyzed analyzed usingusing one-way one-way ANOVA,ANOVA, “a”“a” andand “b”“b” indicateindicate significantsignificant differences, differences, N.S.—not significant, significant, *** *** pp<0.05,< 0.05, ** p<0.01, and * p<0.005. ** p < 0.01, and * p < 0.005.

4.4. InPhytoplankton February, the Community zone classified Structure as and Zone Characteristics I was dominated of Phytoplankton by Eucampia Species, and Zone II consisted of low diatomIt is well populations. known that The spatial communities and temporal affected variability by the river of environmental were classified asparameters Zone III. Thecan stationsaffect adjacentphytoplankton to the estuary community of the structure Nakdong in coastal River were waters. high During in Chaetoceros October, ourand clusterNitzschia analysis(first and dominated MDS groups)analysis along separated the Nakdong the phytoplankton Estuary, ascommunity well as Stephanodiscus into three majorand groupsThalassiosira (p < 0.001).(second Most dominated stations groups).had high In densities particular, of thethe diatomcentric diatomE. zodiacus Chaetoceroswas the spp. predominant (including species C. didymus at the and inner C. coastal lorenzianus regions), ofand Zone this I.is Based known on as oura cosmopolitan CCA results, species. the abundance In particular, of E. diatom zodiacus specieshad accounted a positive for correlation 92.7% of the with total phytoplankton species, and four diatoms (Chaetoceros spp., Skeletonema spp., E. zodiacus, and Pseudo-nitzschia spp.) accounted for approximately 84.6% of the total. The second most dominant species were Skeletonema spp. and E. zodiacus at Zone I, Pseudo-nitzschia spp. at Zone II, and small Cryptomonas spp. at Zone III. Based on our CCA, the abundances of Chaetoceros, Eucampia, and Skeletonema had positive correlations with Chl-a, implying that they were responsible for the high phytoplankton biomass during the autumn. Baek et al. [2] reported that a large introduction of nutrients following heavy precipitation in the inner area of Gwangyang Bay (Korea) led to a large bloom of diatoms (especially Skeletonema spp.) during summer. Tsuchiya et al. [19,20] demonstrated 18 Sustainability 2020, 12, 2781 19 of 21

Chl-a, pH, and DO and a negative correlation with water temperature; this implies that this species contributed to the total phytoplankton biomass when the water temperature was low. E. zodiacus has a worldwide distribution, and sometimes forms dense populations during the relatively low temperatures of winter and early spring [2,27]. Nishikawa et al. [28] reported that E. zodiacus was present throughout the year in temperate coastal waters and was most abundant from February to April, in agreement with our results. Additionally, based on current findings, a high biomass of phytoplankton (mostly E. zodiacus) at Zone I in winter is thought to lead to an increase in pH level and DO concentration, resulting in a positive correlation between this diatom and these two chemical factors. Moreover, Stephanodiscus hantzschii, a harmful freshwater diatom, mainly appeared when salinity was low and nutrient levels were high. In particular, the abundance of S. hantzschii had a positive correlation with nitrogen nutrients and a negative correlation with salinity, thus explaining their higher abundance when there is a continuous overflow of freshwater. Baek et al. [9] reported that a high density of the S. hantzschii was present in Nakdong estuarine water, and this is known as a winter-blooming species in Korea [29]. In addition, this harmful freshwater diatom can tolerate low levels of salinity and can even survive in the saltwater conditions of the Nakdong Estuary. This explains the maintenance of abundant S. hantzschii in estuarine waters. In conclusion, multiple environmental variables present in a coastal area have complex effects on the phytoplankton community. We analyzed these environmental variables in Korean coastal waters to better understand the ecological dynamics of phytoplankton following Typhoon Chaba (October 2016) and a winter water mixing event (February 2017), with a focus on physical, chemical, and biological variables. The changes in the phytoplankton community structure had clear correlations with some of the same environmental factors in all three coastal zones, although there were slight differences between autumn and winter. Nutrient dynamics fluctuated due to Typhoon Chaba. The resulting inflow of fresh water from the estuary area and the large amount of freshwater from the Nakdong River had large effects on the phytoplankton community in the southern sea of Korea during the autumn of 2016. Moreover, the introduction of abundant nutrients into the euphotic layers during a winter mixing period coincided with a high biomass and reproduction of diatoms and the vertical dispersal of these cells. Our results thus indicate that internal and external environmental factors triggered changes in the phytoplankton community during Typhoon Chaba and the subsequent winter turbulence event in Korean coastal waters.

Author Contributions: Conceptualization, data curation, formal analysis, investigation and writing—original draft— S.H.B., M.L., and B.S.P.; data curation and writing—review and editing—B.S.P. and Y.K.L. All authors have read and agreed to the published version of the manuscript. Funding: This research was supported by the Basic Core Technology Development Program for the Oceans and the Polar Regions of the National Research Foundation (NRF) funded by the Ministry of Science, ICT & Future Planning (grant number NRF-2016M1A5A1027456), and a KIOST projects (PE99812). Conﬂicts of Interest: The authors declare no conﬂict of interest.

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