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Article

Diel Variation of Phytoplankton Communities in the Northern South China Sea under the Effect of Internal Solitary Waves and Its Response to Environmental Factors

by
Zhenyu Guan
1,
Ruping Ge
2,
Yunxia Li
1,
Li Zou
2 and
Shimin Yang
1,*
1
College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China
2
College of Environmental Science and Engineering, Ocean University of China, Qingdao 266003, China
*
Author to whom correspondence should be addressed.
Water 2023, 15(13), 2422; https://doi.org/10.3390/w15132422
Submission received: 26 May 2023 / Revised: 26 June 2023 / Accepted: 28 June 2023 / Published: 30 June 2023
(This article belongs to the Special Issue Conserving Biodiversity in Aquatic Ecosystems: Challenges and Solutions)
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Abstract

:
Internal solitary waves (ISWs) are a common marine internal wave phenomenon in the northern South China Sea that cause significant changes in environmental factors and affect phytoplankton communities. This study investigates the short-term response of phytoplankton communities affected by ISWs based on day–night continuous sampling analysis of the sea area following the passage of an internal wave. The results revealed that, due to the IW-mediated transport of nutrients from deeper to shallower layers, the cell abundance of most small- and medium-volume phytoplankton significantly increased after the passage of ISWs. Using a method based on functional traits, we categorized phytoplankton into four functional groups. Moreover, this study revealed the differences in functional group changes in phytoplankton before and after ISWs. The abundance of mixotrophic phytoplankton in the community decreased, whereas autotrophic and heterotrophic phytoplankton increased.

1. Introduction

Traditionally, the physical mechanisms influencing the structure of marine phytoplankton communities have not been limited to the diapycnal mixing of shallow- and deep-sea waters [1]. To date, many new influencing mechanisms have been found, including the actions of eddies [2], tropical cyclones [3], and propagating planetary waves [4,5]. Internal waves (IW) represent another mechanism that significantly affects the structures of marine phytoplankton communities. As a type of internal wave, internal solitary waves (ISWs) are the most striking; thus, they are the focus of this study. ISWs are solitary waves propagating within a layer of discontinuous density typically formed between two fluid layers. These waves often appear at oceanic ridges, such as the edges of continental shelves, straits, bays, and estuaries, and have large amplitudes and lengths [6]. ISWs transport energy and matter into the ocean, thereby significantly affecting the marine environment, ecology, and engineering. Thus, ISWs are critical for studying the structure of marine phytoplankton communities. However, recent research on internal waves has mainly focused on physics rather than biochemistry [7,8]. Although there have been reports on the enhancement of biological productivity in shallow- and deep-water areas by ISWs, this is primarily based on the concentration of chlorophyll a. Research on changes in the construction of marine phytoplankton communities before and after the passage of ISWs is limited [7,9,10,11,12].
The South China Sea, the third largest marginal sea globally, is situated in a low-latitude area and is part of the tropical deep sea. As a region frequently visited by internal waves, scientists often observe ISWs with significant amplitudes and lengths. These ISWs promote the mixing of the upper and lower layers of seawater, causing nutrient-rich cold water to rise from deep to shallow depths, which has a significant effect on the marine ecological environment in this area. The internal waves in the South China Sea originate from the Luzon Strait, from which they propagate westward to the northern part of the South China Sea [13,14,15]. Internal waves have been widely detected from sea surface roughness in Synthetic Aperture Radar (SAR) images [8]. During their extension into the South China Sea, the internal waves encountered the Dongsha Atoll on the Dongsha Plateau. The depth of the Dongsha Plateau is approximately 600 m; however, it abruptly ends in the south, where the depth increases to over 2000 m. These internal waves can freely propagate in this area and finally dissipate onto the continental shelf on the western side of the South China Sea [8,16]. The natural environment around the Dongsha Atoll provides a unique venue for studying how ISWs enhance marine phytoplankton productivity, particularly the dissipation zone north of the Dongsha Atoll and the transmission zones to the east and south [12]. To further study the effect of internal waves on marine phytoplankton communities, we selected two typical areas in the southwest direction of Dongsha Atoll for our research: Station Q40 at a depth of 668 m and Station Q39 at a depth of 1423 m (see Figure 1). We conducted diurnal time-series observations and analyses at these two stations. Both stations were located in the transmission zones of the ISWs and the ISWs that passed through during this period. By comparing the changes in chlorophyll a, temperature, salinity, nitrogen, phosphates, dissolved oxygen (DO), dissolved organic carbon (DOC), and the structure of marine phytoplankton communities before and after the passage of ISWs, we speculated the specific effect of ISWs on the composition of marine phytoplankton communities and the environment.
In this study, we utilized diurnal time-series observations to investigate the influence of ISWs on the summer phytoplankton community structure in the southwestern sea area of the Dongsha Atoll. We chose to observe the summer (September) community structure for two main reasons: Firstly, the shallower mixed layer and sharp thermocline in this sea area during summer are conducive to the formation of ISWs [17,18]. Secondly, studying the summer allows us to avoid the masking or diluting effect that strengthens the vertical mixing of seawater under prevailing winter monsoons on the influence of ISWs [19]. During the observation period, we successfully captured diurnal changes in the phytoplankton community in the northern South China Sea under the effect of ISWs, as well as their responses to environmental factors. Through this research, we aimed to explore how dynamic environmental processes influence environmental factors and, in turn, affect the spatiotemporal distribution of the phytoplankton community structure in the northern South China Sea.

2. Materials and Methods

2.1. Field Data Collection

The observation period for this study was from 1 September to 4 September 2020, at stations Q40 (115°12.75′ E, 20°5.46′ N) and Q39 (115°16.26′ E, 19°53.44′ N). Observations were made every 3 h at station Q40 and every 4 h at station Q39, with a 24 h observation cycle at each station. Water samples were collected using a SeaBird 911 Plus CTD water sampler (Sea-Bird Electronics Inc., Bellevue, WA, USA), and 2% Lugol’s solution was added on-site for fixation. The temperature, dissolved oxygen data, and salinity were obtained from the CTD. Chlorophyll a data were collected via fluorescence photometry using an F-4500 fluorescence spectrophotometer (HITACHI, Tokyo, Japan). Nutrient data for (NH4+, NO2−, NO3−, PO43−, SiO32−) Dissolved nitrate (NO3−), nitrite (NO2−), ammonia (NH4+), and phosphate (PO43−) were measured by using the SEAL-AA3 Auto Continuous Flow Analytical System. Dissolved inorganic nitrogen (DIN) is the sum of NO3−, NO2− and NH4+. Silicate was measured with a Nutrient Auto Analyzer (Clever chem/anna, DeChem-Tech, Germany) based on silicon molybdenum blue spectrophotometry, and the relative error was less than 0.3% in duplicate measurements. Current data were measured using a 75 kHz Self-contained Acoustic Doppler Current Profiler (ADCP) to determine the specific time of ISWs passage.

2.2. Phytoplankton Data

Phytoplankton samples were analyzed using the Utermöhl method [20]. Samples were concentrated in the laboratory and poured into a Hydro-Bios counting chamber (Hydro-Bios, Kiel, Germany). Cell counts and species identification were performed using a NikonTS100 inverted microscope (Nikon, Tokyo, Japan), and we consulted relevant books for their identification [21,22,23,24].

2.3. Data Analysis

The diversity of the phytoplankton communities was analyzed using the following formula:
The Shannon–Wiener diversity index (H′) [25] was used to calculate the biodiversity index:
H ′ = − ∑ i = 1 S P i log 2 P i
Species evenness index (Pielou index) [26]:
J = H ′ / log 2 S
Dominance degree of species (species with dominance degree ≥ 0.02 are dominant species) [27]:
Y = n i / N × f i
where ni is the number of individuals of the i-th species, N is the entire number of species in the collected sample, S is the total number of species in the sample, Pi is the ratio of the number of the i-th species (ni) to the total number (N), fi is the frequency of occurrence of the species.
We divided the phytoplankton community into four functional groups based on the three functional traits of phytoplankton species and analyzed representative phytoplankton species (33 species) with a cell abundance of >260 cells/L. Based on previous studies, the selected traits included cell size (CS) [28], temperature adaptation (TA) [29], and nutrition mode (NM) [30]. CS was divided into four volume categories: <10,000 μm3, 10,000–100,000 μm3, 100,000–1,000,000 μm3, and >1,000,000 μm3. The TA was divided into three types: northern temperate species, eurythermal species, and warm water species. NM was classified into three types: autotrophic, heterotrophic, and mixotrophic (photophagotrophy).
The data were organized into a species × trait data frame (Table A1), and a dissimilarity matrix was calculated using Gower’s distance. Species were clustered based on the similarities and differences in functional traits using Ward’s method. The Elbow method was used to determine the optimal number of functional groups (clusters). Cluster analysis was performed using the Factoextra package in the R statistical software [31,32].
The relationship between the groups of algae after cluster analysis and environmental factors was determined using a generalized additive models (GAM) through the “gam” function in the mgcv package in R [33]. The environmental factors included chlorophyll a, temperature, phosphate, salinity, total nitrogen, and dissolved oxygen. Before analysis, the cell abundances of chlorophyll a and phytoplankton underwent logarithmic transformation (log 10) [34]. The best combination of environmental variables was selected using stepwise regression [35]. The GAM was first run using a method that included all explanatory variables. The optimal model was selected by minimizing the generalized cross-validation (GCV) criterion during the fitting process [33].

3. Results

3.1. Composition of Phytoplankton Species

In this study, 3 phyla, 71 genera, and 186 species (including variants and forms) of phytoplankton were identified (Table A2). Among them, there were 39 genera and 85 species in the phylum Bacillariophyta, accounting for 45.70% of the total species, and the dominant group was the genus Chaetoceros, with 24 species, accounting for 28.24% of the diatom species. The phylum Dinophyta comprises 30 genera and 99 species, accounting for 53.23% of the total number of species. Among these, the genus Oxytoxum had 17 species, accounting for 17.17% of the dinoflagellate species, whereas the genus Gonyaulax had 11 species, accounting for 11.11% of the dinoflagellate species. The phylum Chrysophyta comprised two genera and two species, accounting for 1.07% of the total species.

3.2. Vertical Distribution of Phytoplankton under the Influence of ISWs

In the surveyed area, the phytoplankton cell abundance at the Deep Chlorophyll Maximum (DCM) ranged from 5.33 × 102 to 41.34 × 102 cells/L, with an average of 18.55 × 102 cells/L (Figure 2). Among them, the diatom cell abundance ranged from 2.30 × 102 to 34.73 × 102 cells/L, with an average of 14.98 × 102 cells/L, accounting for 80.78% of the total cell abundance, occupying an absolutely dominant position (Figure 2). The dinoflagellate cell abundance ranged from 0.57 × 102 to 8.84 × 102 cells/L, with an average of 3.32 × 102 cells/L, accounting for 17.92% of the entire cell abundance (Figure 2). The average cell abundance of Chrysophyta was 0.24 × 102 cells/L, accounting for 1.30% of the entire cell abundance.

3.2.1. Q40 Station

According to the ADCP observation data, the observation times of Q40-1 and Q40-2 occurred before the arrival of the ISWs, whereas Q40-3, Q40-4, Q40-5, Q40-6, Q40-7, and Q40-8 occurred after the passage of the ISWs. Before ISWs at Q40 station, the total phytoplankton cell abundance in the DCM layer ranged from 5.33 × 102 to 15.07 × 102 cells/L, with an average of 10.20 × 102 cells/L. Among them, diatom cell abundance ranged from 4.76 × 102 to 12.92 × 102 cells/L, with an average of 8.84 × 102 cells/L, accounting for 86.67% of total cell abundance; dinoflagellate cell abundance ranged from 0.57 × 102 to 1.01 × 102 cells/L, with an average of 0.79 × 102 cells/L, accounting for 7.75% of entire cell abundance; and the average cell abundance of Chrysophyta was 0.57 × 102 cells/L, accounting for 5.58% of total cell abundance. After ISWs at Q40 station, the total phytoplankton cell abundance in the DCM layer ranged from 13.23 × 102 to 41.34 × 102 cells/L, with an average of 26.23 × 102 cells/L, a significant increase from before the arrival of ISWs. Among them, diatom cell abundance ranged from 9.48 × 102 to 34.73 × 102 cells/L, with an average of 22.31 × 102 cells/L, accounting for 85.06% of total cell abundance, with diatoms still in an absolutely dominant position; dinoflagellate cell abundance ranged from 0.74 × 102 to 6.26 × 102 cells/L, with an average of 3.78 × 102 cells/L, accounting for 14.41% of entire cell abundance; the average cell abundance of Chrysophyta was 0.14 × 102 cells/L, accounting for 0.53% of total cell abundance.

3.2.2. Q39 Station

Due to problems with the ADCP observations, we did not obtain data on ISWs. However, because the distance and observation times between stations Q39 and Q40 were relatively close, we speculate that ISWs passed during the observation period. The total phytoplankton cell abundance in the DCM layer at Q39 station ranged from 7.92 × 102 to 30.41 × 102 cells/L, with an average of 14.35 × 102 cells/L. Among them, diatom cell abundance ranged from 2.30 × 102 to 20.75 × 102 cells/L, with an average of 10.45 × 102 cells/L, accounting for 72.82% of total cell abundance, occupying an absolutely dominant position; dinoflagellate cell abundance ranged from 0.66 × 102 to 8.84 × 102 cells/L, with an average of 3.66 × 102 cells/L, accounting for 25.51% of entire cell abundance; the average cell abundance of Chrysophyta was 0.24 × 102 cells/L, accounting for 1.67% of entire cell abundance.

3.3. Effect of ISWs on Dominant Species

The dominant species at Q40 station before the ISWs were Nitzschia spp., Chaetoceros lorenzianus, Chaetoceros spp., Nitzschia longissima, Skeletonema costatum, Thalassionema nitzschioides, Gyrodinium dominans, Oxytoxum curvatum, Pronoctiluca pelagica, Protoperidinium sp., and Dictyocha speculum. Among them, Nitzschia spp. (14.49%) and T. nitzschioides (13.68%) were the dominant species before the arrival of the ISWs at Q40 station. The average cell abundance of Nitzschia spp. at 5 m, the DCM layer, and 200 m were 49.15 cells/L, 125.4 cells/L, 47.52 cells/L, respectively (Figure 3), and those of T. nitzschioides were 0 cells/L, 209.7 cells/L, 0 cells/L.
After the passage of ISWs, the dominant species at Q40 station were: Chaetoceros debilis, N. longissima, Nitzschia spp., T. nitzschioides, Thalassiosira spp., Thalassiothrix longissima, G. dominans, and Karenia mikimotoi. Nitzschia spp. (14.80%) were the dominant species after the passage of ISWs at Q40 station. The average cell abundances of Nitzschia spp. at 5 m, the DCM layer, and at 200 m were 163.86 cells/L, 355.88 cells/L, 9.49 cells/L, respectively. The cell abundance of Nitzschia spp. increased significantly compared to that before the arrival of ISWs, but the proportion of cell abundance did not change significantly (Figure 3).
The dominant species at Q39 station were Nitzschia spp., T. nitzschioides, Thalassiosira spp., Scrippsiella trochoidea, Chaetoceros rostratus, Cochlodinium polykrikoides, G. dominans, Gyrodinium instriatum, Oxytoxum crassum, Oxytoxum turbo, and P. pelagica.
The degree of dominance indicates the status and role of a species in a community. In the studied marine area, Nitzschia spp. was the species with absolute advantage, with a high occurrence frequency of up to 100% (Figure 3).

3.4. Changes in Phytoplankton Species

Based on all the dominant phytoplankton species mentioned in Section 3.3, we calculated the proportions of each dominant species at different periods to show the community composition of the dominant species at each period (including 10 diatom species, 10 dinoflagellate species, and 1 chrysophyte species). Nitzschia spp., G. dominans, and T. nitzschioides had relatively high abundances in the DCM layer in all periods, with Nitzschia spp. appearing in all water layers (Figure 4). At Q40 station, C. rostratus and G. instriatum started to appear only after the ISW period. These trends reflect the different responses of phytoplankton community structures to drastic environmental changes.

3.5. Diversity and Evenness Indices

The Shannon–Wiener diversity index and Pielou evenness index were used to measure the stability of the phytoplankton community structure.

3.5.1. Q40 Station

Before the ISWs, the Shannon–Wiener diversity index range for the phytoplankton in the 5 m water layer was 3.40–4.28, with an average of 3.84, and the evenness index ranged between 0.81 and 0.89, with an average of 0.85. The diversity index in the DCM water layer ranged from 3.57 to 3.65, with an average of 3.61, while the evenness index ranged from to 0.88–0.91, with an average of 0.90. After the ISWs, the Shannon–Wiener diversity index range for the phytoplankton in the 5 m water layer was 3.56–3.90, with an average of 3.74, and the evenness index ranged between 0.75 and 0.83, with an average of 0.78. The diversity index at the DCM water layer ranged from 3.30 to 4.26, with an average of 3.83, and the evenness index ranged from 0.73 to 0.85, with an average of 0.82.

3.5.2. Q39 Station

The Shannon–Wiener diversity index range for the phytoplankton in the 5 m water layer was 1.36–4.08, with an average of 2.87, and the evenness index ranged between 0.67 and 0.87, with an average of 0.76. The highest average values of the diversity index and evenness index were in the DCM water layer, with the diversity index ranging from 2.77 to 4.27, averaging 3.66, and the evenness index ranging from 0.68 to 0.94, with an average of 0.81.

3.6. Community Structure Cluster Analysis

Four phytoplankton functional groups were identified based on these three functional traits (Figure 5; Table 1).
Group 1 includes small-to medium-volume phytoplankton, eurythermal species, and heterotrophic dinoflagellates (Table 1). At the Q40 station, the average cell abundance of this group was 26.14 cells/L before the ISWs and 80.90 cells/L after the ISWs. The cell abundance in Group 1 was significantly lower before ISWs treatment than after ISWs treatment.
Group 2 included small-to medium-volume phytoplankton, species ranging from temperate to eurythermal to warm water, and autotrophic diatoms and dinoflagellates (Table 1). At the Q40 station, the average cell abundance of this group was 25.21 cells/L before the ISWs and 54.18 cells/L after the ISWs. The cell abundance in Group 2 was significantly lower before ISWs treatment than after ISWs treatment.
Group 3 included small-volume phytoplankton, species ranging from eurythermal to warm water, and mixotrophic chrysophytes and dinoflagellates (Table 1). At the Q40 station, the average cell abundance of this group was 49.59 cells/L before the ISWs and 26.09 cells/L after the ISWs. The cell abundance in Group 3 was significantly higher before ISWs treatment than after ISWs treatment.
Small-volume phytoplankton, species ranging from eurythermal to warm water, and autotrophic diatoms and dinoflagellates (Table 1) were assigned to Group 4. At the Q40 station, the average cell abundance of this group was 46.54 cells/L before the ISWs and 86.81 cells/L after the ISWs. The cell abundance in Group 4 was significantly lower before ISWs treatment than after ISWs treatment.

3.7. Correlation between Phytoplankton and Environmental Factors

Our Generalized Additive Model (GAM) annalysis revealed relationships between the four functional groups and environmental variables (Figure 6 and Table 2). Temperature exhibited a negative correlation with Groups 3 and 4. Group 1 showed a positive correlation below 22 °C but became negatively correlated above 22 °C. Group 2 showed a positive correlation below 25 °C but became negatively correlated above 25 °C. The concentration of chlorophyll a showed a negative correlation with group 1 but exhibited a positive correlation with group 4. Although the influence of phosphate on the four functional groups was minimal, the total nitrogen concentration showed a negative correlation with all four groups. DOC showed a positive correlation with all functional groups, whereas dissolved oxygen first showed a positive correlation with Group 3 and then became negatively correlated.

4. Discussion

4.1. Effect of ISWs on Phytoplankton and Dominant Species

The ISWs in the studied sea area are of the sinking type. When ISWs pass through this area, they mix the upper and lower water layers. This mixing process transports water bodies from deeper layers, which contain higher nutrient concentrations, to shallower layers. This provides more abundant nutrients for phytoplankton in the shallower layers, thereby stimulating their growth and reproduction [36]. As we observed, after the ISWs, the DCM layer rose to varying degrees at different time points (Figure 2). Furthermore, the adaptability of different phytoplankton species, particularly the dominant species, to environmental conditions varies. For instance, after the passage of ISWs, the average cell abundance of Nitzschia spp. increased in all water layers; however, its average proportion did not change significantly. This is because Nitzschia spp. have better adaptability to environmental changes [37] and can maintain a relatively high growth rate under new environmental conditions caused by ISWs. However, other species adapt differently to environmental changes; therefore, the proliferation efficiency of each species after ISWs is not balanced. Moreover, changes in cell abundance caused by ISWs are also affected by the interactions between species. For example, different species of phytoplankton compete for nutrients [38], and species that can use newly supplied nutrients more quickly or effectively will have greater growth after treatment with ISWs; thus, they have a stronger competitive advantage.

4.2. Response of Four Functional Groups to Environmental Factor Changes under the Effect of ISWs

According to the GAM analysis, temperature has a positive correlation with the heterotrophic dinoflagellate population (Group 1), which consists of species from the north temperate zone (positive correlation above 22 °C and a negative correlation below 22 °C); it also has a positive correlation with the autotrophic diatoms and dinoflagellates (Group 2) consisting of species from the north temperate zone—wide-temperature zone—warm water zone before 24 °C, and then becomes negatively correlated. This is because the physiological and metabolic activities of some dinoflagellates are enhanced within suitable temperature ranges, thereby increasing their growth and reproduction [39]. However, when the temperature exceeds this range, it can damage the physiological and metabolic activities of dinoflagellate cells, thereby affecting their survival. In addition, heterotrophic dinoflagellates rely on the ingestion of organic matter to obtain energy, and high temperatures can affect their ability to capture organic matter, further affecting their survival [40].
In the oligotrophic northern South China Sea, the cell abundances of medium- and large-sized phytoplankton were far lower than that of small-sized phytoplankton, and chlorophyll a was significantly positively correlated with small-sized autotrophic phytoplankton (Group 4). When water temperatures are high, small phytoplankton usually have higher growth rates, which allow them to rapidly utilize resources in nutrient-poor areas, thereby gaining an advantage in competition [41,42]. Moreover, a high-temperature environment is more suitable for small phytoplankton because it has a higher surface-area-to-volume ratio, which enables them to absorb light and nutrients [43].
DOC is positively correlated with most medium-sized dinoflagellates (Group 1) and significantly positively correlated with small dinoflagellates and chrysophytes (Group 3). Compared with diatoms, medium- and small-sized dinoflagellates have higher productivity in the ocean [44], faster growth rates, and stronger adaptability [39]. Column mixing caused by ISWs not only brings surface water to the deep layers but also brings nutrient-rich deep water to the upper layers. This provides dinoflagellates and chrysophytes with more nutrients, increasing their productivity and biomass and thus increasing the concentration of DOC.
Dissolved oxygen is initially positively correlated and then negatively correlated with small mixotrophic dinoflagellates and chrysophytes (Group 3). This phenomenon is related to the balance between respiration and photosynthesis in the phytoplankton. Dinoflagellates and chrysophytes perform photosynthesis during the day, absorbing carbon dioxide and releasing oxygen, thus increasing the concentration of dissolved oxygen and resulting in a positive correlation. However, at night or in environments with insufficient light, these phytoplankton perform respiration, consume oxygen, and produce carbon dioxide. Thus, the concentration of dissolved oxygen decreases, exhibiting a negative correlation [45]. Moreover, because they are mixotrophic, they switch to heterotrophic nutrition when light is insufficient [30], further consuming dissolved oxygen.

5. Conclusions

ISWs significantly alter the phytoplankton community structure. Downwelling ISWs passing through marine areas cause water column mixing, bringing nutrient-rich deep water to the surface, providing phytoplankton with more nutrients, stimulating their growth and reproduction, and significantly increasing the abundances of most medium- and small-sized diatoms and dinoflagellates. Furthermore, according to our GAM analysis, under the effect of ISWs, changes in environmental factors such as temperature, chlorophyll a, DOC, and dissolved oxygen have different effects on different functional groups of phytoplankton, resulting in a decrease in the abundance of mixotrophic phytoplankton and an increase in the abundance of autotrophic and heterotrophic phytoplankton in the phytoplankton community.

Author Contributions

Conceptualization, Z.G.; methodology, Z.G., R.G.; investigation, L.Z.; data curation, Y.L.; writing—original draft preparation, Z.G.; writing—review and editing, Z.G., S.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the National Natural Science Foundation of China (No. 31750001). The data and sample collection for this study were based on the shared voyage of the National Natural Science Foundation of China (Voyage number: NORC2019-05).

Data Availability Statement

Not applicable.

Acknowledgments

“Dong Fang Hong 3” research vessel, for which we would like to express our gratitude.

Conflicts of Interest

We state that there were no conflict of interest related to this study. All funding information and people who provided help during this study have been mentioned in the manuscript.

Appendix A

Table
Table A1. Species × trait data frame.
Table A1. Species × trait data frame.
TaxaVolume (μm3)Nutritional TypeEcological Group
Group1
Cochlodinium spp.100,000–1,000,000HNBT
Gymnodinium spp.<10,000HNBT
Protoperidinium spp.10,000–100,000HNBT
Karenia mikimotoi10,000–100,000HNBT
Karenia brevis10,000–100,000HNBT
Gyrodinium dominans10,000–100,000HNBT
Gyrodinium spp.10,000–100,000HNBT
Group2
Chaetoceros debilis<10,000ANNT
Chaetoceros densus10,000–100,000ANNT
Thalassiothrix longissima10,000–100,000ANBT
Synedra spp.10,000–100,000ANBT
Chaetoceros lorenzianus10,000–100,000ANBT
Chaetoceros spp.10,000–100,000ANBT
Chaetoceros rostratus10,000–100,000ANBT
Oxytoxum turbo10,000–100,000ANWW
Group3
Dictyocha speculum<10,000MNBT
Pronoctiluca pelagica<10,000MNWW
Group4
Pseudo-nitzschia pungens<10,000ANWW
Oxytoxum mitra<10,000ANWW
Oxytoxum curvatum<10,000ANWW
Chaetoceros femur<10,000ANWW
Chaetoceros atlanticus<10,000ANWW
Chaetoceros atlanticus var. neapolitana<10,000ANWW
Thalassiosira spp.<10,000ANBT
Thalassionema nitzschioides<10,000ANBT
Thalassionema frauenfeldii<10,000ANBT
Skeletonema costatum<10,000ANBT
Scrippsiella trochoidea<10,000ANBT
Prorocentrum minimun<10,000ANBT
Prorocentrum dentatum<10,000ANBT
Oxytoxum crassum<10,000ANBT
Nitzschia longissima<10,000ANBT
Nitzschia spp.<10,000ANBT
Table
Table A2. Species list of the phytoplankton assemblage.
Table A2. Species list of the phytoplankton assemblage.
DinophytaProtoperidinium simulum (Paulsen) Balech, 1974
Alexandrium minutum Halim, 1960Protoperidinium spp.
Alexandrium spp.Pyrocystis noctiluca Murray ex Haeckel
Alexandrium tamarense (Lebour) Balech, 1985Pyrophacus steinii (Schiller) Wall & Dale
Amphisolenia schroederi KofoidScrippsiella trochoidea (Stein) Balech ex Loeblich III
Blepharocysta splendor-maris (Ehrenberg) EhrenbergBacillariophyta
Ceratocorys bipes (Cleve) Kofoid, 1910Actinoptychus hexagonus Grunow in Schmidt, 1874
Ceratocorys horrida Stein, 1883Actinoptychus senarius (Ehr.) Ehrenberg
Cochlodinium brandtii WulffArachnoidiscus ehrenbergii Bailey ex Ehrenberg
Cochlodinium polykrikoides MargelefAsteromphalus flabellatus (Brébisson) Greville, 1859
Cochlodinium spp.Bacteriastrum comosum Pavillard
Corythodinium belgicae (Meunier) F.J.R.Taylor, 1976Bacteriastrum furcatum Shadbolt, 1854
Corythodinium carinatum (Gaarder) F.J.R.Taylor, 1976Bacteriastrum hyalinum Lauder
Corythodinium constrictum (Stein) TaylorBacteriastrum mediterraneum Pavillard
Corythodinium curvicaudatum (Kofoid) F.J.R.Taylor, 1976Biddulphia pellucida Castracane, 1886
Corythodinium latum (Gaarder) F.J.R.Taylor, 1976Cerataulina bergonii Ostenfeld, 1903
Corythodinium reticulatum (Stein) Taylor, 1976 Chaetoceros affinis Lauder
Corythodinium spp.Chaetoceros atlanticus Cleve
Corythodinium tesselatum (Stein) Loeblich Jr.& Loeblich IIIChaetoceros atlanticus var. neapolitana (Schröder) Hustedt
Dinophysis acuminata Claparède et LachmannChaetoceros buceros Karsten
Dinophysis favus (Kofoid & Michener) BalechChaetoceros castracanei Karsten
Dinophysis oviformis Chen & Ni, 1988Chaetoceros coarctatus Lauder
Dinophysis parva SchillerChaetoceros constrictus Gran
Dinophysis spp.Chaetoceros danicus Cleve
Diplopsalopsis bomba (Stein ex Jorgensen) Dodge & ToriumiChaetoceros debilis Cleve
Dolichodinium lineatum (Kofoid & Michener) Kofoid & Adamson, 1933Chaetoceros densus (Cleve) Cleve, 1899
Gonyaulax cochlea Meunier, 1919Chaetoceros denticulatus Lauder
Gonyaulax kofoidii Pavillard, 1909Chaetoceros distans Cleve
Gonyaulax macroporus Mangin, 1922Chaetoceros femur Schütt
Gonyaulax minuta Kofoid & Michener, 1911Chaetoceros hirundinellus Qian
Gonyaulax monospina Rampi, 1951Chaetoceros lauderi Ralfs
Gonyaulax ovalis Schiller, 1929Chaetoceros lorenzianus Grunow
Gonyaulax pacifica Kofoid, 1907Chaetoceros messanensis Castracane
Gonyaulax polygramma Stein, 1883Chaetoceros paradoxus Cleve
Gonyaulax spinifera (Claparede & Lachmann) Diesing, 1866Chaetoceros pelagicus Cleve
Gonyaulax spp.Chaetoceros peruvianus Brightwell
Gonyaulax turbynei Murray & Whitting, 1899Chaetoceros pseudodichaeta Ikari
Gymnodinium spp.Chaetoceros rostratus Lauder
Gyrodinium dominans HulbertChaetoceros saltans Cleve
Gyrodinium falcatum Kofoid & Swezy, 1921Chaetoceros spp.
Gyrodinium instriatum Freudenthal et LeeCorethron criophilum Castracane
Gyrodinium spirale (Bergh) Kofoid et SwezyCoscinodiscus asteromphalus Ehrenberg
Gyrodinium spp.Coscinodiscus granii Grough
Heterocapsa triquetra (Ehrenberg) Stein, 1883Coscinodiscus jonesianus (Greville) Ostenfeld
Heterodinium agassizii KofoidCoscinodiscus radiatus Ehrenberg
Heterodinium milneri (Murray & Whitting) Kofoid, 1906Coscinodiscus spp.
Heterodinium whittingiae Kofoid, 1906Cylindrotheca closterium (Ehrenberg) Reimann & J.C. Lewin, 1964
Histioneis gregoryi BöhmDiploneis bombus Ehrenberg
Karenia brevis (Davis) G.Hansen & MoestrupEucampia cornuta (Cleve) Grunow
Karenia mikimotoi Hansen & MoestrupEunotogramma debile Grunow in Van Heurck, 1883
Lingulodinium polyedrum (Stein) DodgeFragilaria spp.
Lissodinium spp.Fragilariopsis doliolus (Wallich) Medlin & Sims
Lissodinium taylorii Carbonell-Moore, 1993Guinardia flaccida (Castracane) Peragallo
Neoceratium boehmii (Graham et Bronikovsky)Guinardia striata (Stolterfoth) Hasle et al.
Neoceratium horridum (Gran) Gómez, Moreira & López-Garcia Helicotheca tamesis (Shrubsole) Ricard
Neoceratium kofoidii (Jörgensen) Gómez, Moreira & López-Garcia Hemiaulus hauckii Grunow ex Van Heurck, 1882
Neoceratium longipes (Bailey) Gómez, Moreira & López-Garcia Hemiaulus membranacus Cleve
Neoceratium setaceum (Jörgensen) Gómez, Moreira & López-Garcia Hemiaulus sinensis Greville
Neoceratium teres (Kofoid) Gómez, Moreira & López-Garcia Leptocylindrus danicus Cleve
Ornithocercus thumii (Schmidt) Kofoid & SkogsbergLeptocylindrus mediterraneus (H. Peragallo) Hasle
Oxytoxum crassum SchillerMastogloia rostrata (Wallich) Hustedt
Oxytoxum curvatum (Kofoid) Kofoid, 1911Navicula membranacea Cleve, 1897
Oxytoxum depressum Schiller, 1937Navicula spp.
Oxytoxum elongatum Wood, 1963Nitzschia longissima (Brébisson) Ralfs, 1861
Oxytoxum laticeps SchillerNitzschia lorenziana Grunow
Oxytoxum longiceps SchillerNitzschia panduriformis Hustedt in Schmidt et al., 1921
Oxytoxum milneri Murray & Whitting, 1899Nitzschia spp.
Oxytoxum mitra Stein, 1883Odontella sinensis (Greville) Grunow
Oxytoxum mucronatum Hope, 1954Palmeria hardmaniana Greville
Oxytoxum parvum Schiller, 1937Paralia sulcata (Ehrenberg) Cleve, 1873
Oxytoxum sceptrum (Stein) SchröderPinnularia spp.
Oxytoxum scolopax SteinPlanktoniella formosa Qian & Wang
Oxytoxum sphaeroideum SteinPlanktoniella sol Qian et Wang
Oxytoxum spp.Pleurosigma acutum Norman
Oxytoxum turbo KofoidPleurosigma pelagicum Peragallo
Oxytoxum variabilis SchillerPleurosigma spp.
Palaeophalacroma unicinctum Schiller, 1928Pseudo-nitzschia pungens (Grunow ex Cleve) Hasle
Podolampas bipes SteinRhabodonema adriaticum Kützing
Podolampas palmipes SteinRhizosolenia alata f. indica (Peragallo) Ostenfeld
Pronoctiluca pelagica Fabre-DomerqneRhizosolenia bergonii Peragallo
Pronoctiluca spinifera (Lohmann) Schiller, 1932Rhizosolenia sinensis Qian
Prorocentrum compressum (Ostenfeld) AbéRhizosolenia styliformis Brightwell
Prorocentrum dentatum SteinSkeletonema costatum (Greville) Cleve
Prorocentrum lenticulatum (Matzenauer) TaylorSynedra spp.
Prorocentrum lima (Ehrenberg) DodgeThalassionema frauenfeldii (Grunow) Hallegraeff
Prorocentrum minimun (Pavillard) SchillerThalassionema nitzschioides Grunow
Prorocentrum sigmoides BöhmThalassiosira eccentrica (Ehrenberg) Cleve, 1904
Prorocentrum spp.Thalassiosira spp.
Prorocentrum triestinum SchillerThalassiothrix longissima Cleve et Grunow
Protoceratium areolatum KofoidTriceratium affine Grunow
Protoperidinium conicum (Gran) BalechTriceratium favus Ehrenberg
Protoperidinium excentricum (Paulsen) Balech, 1974Dictyochophyceae
Protoperidinium orientale (Matzenauer) BalechDictyocha fibula Ehrenberg
Protoperidinium ovum (Schiller) Balech, 1974Dictyocha speculum Ehrenberg
Protoperidinium pyrum (Balech) Balech

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Water 15 02422 g001 550
Figure 1. Time-series observation station locations in the northern South China Sea.
Figure 1. Time-series observation station locations in the northern South China Sea.
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Water 15 02422 g002a 550Water 15 02422 g002b 550
Figure 2. Changes in total cell abundance, diatom cell abundance, and dinoflagellate cell abundance at Q40 and Q39 stations.
Figure 2. Changes in total cell abundance, diatom cell abundance, and dinoflagellate cell abundance at Q40 and Q39 stations.
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Figure 3. The abundance changes of Nitzschia spp. in various water layers.
Figure 3. The abundance changes of Nitzschia spp. in various water layers.
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Figure 4. Percentage Abundance of 21 Dominant Species.
Figure 4. Percentage Abundance of 21 Dominant Species.
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Figure 5. Dendrogram of functional groups of phytoplankton identified on the basis of functional traits (via agglomerative hierarchical clustering).
Figure 5. Dendrogram of functional groups of phytoplankton identified on the basis of functional traits (via agglomerative hierarchical clustering).
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Figure 6. Relationships between abundance of functional groups and environmental factors (temperature, chlorophyll a, dissolved organic carbon, dissolved oxygen) in the area. The shaded areas are the 95% confidence intervals.
Figure 6. Relationships between abundance of functional groups and environmental factors (temperature, chlorophyll a, dissolved organic carbon, dissolved oxygen) in the area. The shaded areas are the 95% confidence intervals.
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Table
Table 1. The functional characteristics of four identified phytoplankton functional groups.
Table 1. The functional characteristics of four identified phytoplankton functional groups.
GroupSpecies
Number		Taxonomic ComponentVolume
(μm3)		Nutritional TypeEcological Group
17Dinoflagellate<10,000, 10,000–1,000,000,
100,000–1,000,000		Heterotrophismeurythermal species
28Diatom, Dinoflagellate<10,000, 10,000–1,000,000AutotrophyBoreal species, eurythermal species, Warm-water species
32Dinoflagellate, Chrysophyceae<10,000Mixotrophismeurythermal species, Warm-water species
416Diatom, Dinoflagellate<10,000Autotrophyeurythermal species, Warm-water species
Table
Table 2. Generalized additive model results for phytoplankton and environmental factors among the four transects.
Table 2. Generalized additive model results for phytoplankton and environmental factors among the four transects.
Groupp-ValueR2GCV
TemperatureChlaPhosphateDINDOCDO
10.0610.4280.5310.2270.0620.6290.5280.421
20.019 *0.001 ***0.3060.6830.0880.0680.8040.322
30.2460.033 *0.8330.0900.007 **0.041 *0.3130.498
40.2190.008 **0.1240.0560.1710.7050.8350.126
Notes: *** p < 0.001; ** p < 0.01; * p < 0.05.
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Guan, Z.; Ge, R.; Li, Y.; Zou, L.; Yang, S. Diel Variation of Phytoplankton Communities in the Northern South China Sea under the Effect of Internal Solitary Waves and Its Response to Environmental Factors. Water 2023, 15, 2422. https://doi.org/10.3390/w15132422

AMA Style

Guan Z, Ge R, Li Y, Zou L, Yang S. Diel Variation of Phytoplankton Communities in the Northern South China Sea under the Effect of Internal Solitary Waves and Its Response to Environmental Factors. Water. 2023; 15(13):2422. https://doi.org/10.3390/w15132422

Chicago/Turabian Style

Guan, Zhenyu, Ruping Ge, Yunxia Li, Li Zou, and Shimin Yang. 2023. "Diel Variation of Phytoplankton Communities in the Northern South China Sea under the Effect of Internal Solitary Waves and Its Response to Environmental Factors" Water 15, no. 13: 2422. https://doi.org/10.3390/w15132422

APA Style

Guan, Z., Ge, R., Li, Y., Zou, L., & Yang, S. (2023). Diel Variation of Phytoplankton Communities in the Northern South China Sea under the Effect of Internal Solitary Waves and Its Response to Environmental Factors. Water, 15(13), 2422. https://doi.org/10.3390/w15132422

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