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Article

The Impact of Environmental Factors on the Spatiotemporal Heterogeneity of Phytoplankton Community Structure and Biodiversity in the Qiongzhou Strait

by
Chunling Xu
1,2,
Yu Ma
1,2,
Hao Zhang
1,2,
Xiaoming Li
1,2 and
Qi Chen
3,*
1
South China Sea Marine Survey Center, Ministry of Natural Resources, Guangzhou 510300, China
2
Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources, Guangzhou 510300, China
3
Ecological Environmental Engineering Research Center, Guangzhou Pearl River Resources Protection Technology Development Co., Ltd., Guangzhou 510611, China
*
Author to whom correspondence should be addressed.
Water 2023, 15(21), 3792; https://doi.org/10.3390/w15213792
Submission received: 19 September 2023 / Revised: 19 October 2023 / Accepted: 27 October 2023 / Published: 29 October 2023
(This article belongs to the Special Issue Aquatic Plant Ecology: Biodiversity and Ecological Processes)
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Abstract

:
The distribution and variation in environmental factors and the phytoplankton community in the Qiongzhou Strait were investigated in autumn 2015 and spring 2016. The environmental factors were different in the two seasons, as seen when using one-way ANOVA testing, principal component analysis, and cluster analysis. The temperature and levels of dissolved oxygen and soluble solids were higher in autumn, and the ammonia (NH4-N) level was much higher in spring (4.66 ± 0.97 µmol/L), which led to a higher concentration of dissolved inorganic nitrogen in the northeast area. A total of 118 species of phytoplankton were found, with 47 common species in both seasons and more species in spring. Bacillariophyta (diatom) contributed to over 80% of the relative abundance in both seasons. The species of Chrysophyta and Xanthophyta were endemic species in autumn and spring, respectively. Skeletonema costatum and Chaetoceros curvisetus were the common dominant species, with nine dominant species in both seasons. Higher abundance and biodiversity of phytoplankton appeared in spring, and their differences were mostly determined by NH4-N level, hydrodynamic conditions and artificial activities in the coastal area in the Qiongzhou Strait.

1. Introduction

The Qiongzhou Strait is in the South China Sea, separating mainland China from Hainan Island. The strait is approximately 80 km long and 20~40 km wide, with a total area of approximately 2370 square kilometers [1]. The Qiongzhou Strait has a warm climate and abundant precipitation all year round. The overall strait is affected by a tropical monsoon, which is divided into rainy and dry seasons [2]. The rainy season is usually from May to October, and the dry season is from November to April. The Qiongzhou Strait connects the northern part of the South China Sea and Beibu Gulf and is an important channel for water and nutrient exchange [3,4]. Furthermore, its rich natural resources also cause it to be an important area for fishing, oil extraction, and shipping industries [5,6,7]. The northern part of the strait is Xuwen County, Zhanjiang City, and the southern part is Haikou City, which is the most economically developed and populous city on Hainan Island. The strait is home to approximately 20 million people, mainly concentrated in the southern part of the strait. In recent years, it has become an economic development point of focus for southern China.
The functional roles of the Qiongzhou Strait, such as fishery production, economic development, and trade routes, which are closely related to human activities and production and life, directly depend on the environmental conditions and primary productivity of the strait [8]. In addition to human activities in the surrounding sea and land areas, environmental conditions also impact phytoplankton by gradually increasing marine utilization intensity [9]. As the most critical primary producers in marine ecosystems, the health of phytoplankton community structures, such as changes in biomass, biodiversity, and community succession rules, is crucial to the health of marine ecosystems, marine aquaculture resources, the occurrence of harmful algal blooms, and even climate change [10]. However, for such an important region, there is little information or research on the relationship between marine environmental factors and the structure of phytoplankton communities.
In this study, water quality factor and phytoplankton surveys were conducted in the central area of the Qiongzhou Strait in November 2015 and April 2016. Multivariate statistical analyses, such as cluster analysis, PCA, and RDA, were used to study the association between the phytoplankton community and water quality factors in the study area. Our purpose was to (1) investigate the spatial and temporal differences in the abundance, composition, and diversity of phytoplankton in the Qiongzhou Strait during autumn and spring.; (2) explain the major influencing factors and their interrelationships in the succession of phytoplankton communities in the Qiongzhou Strait; (3) provide baseline data on regional environmental factors and phytoplankton communities.

2. Materials and Methods

2.1. Study Area

The study area of this research is in the central part of the Qiongzhou Strait, covering an area of approximately 500 km2 (N20°03′48″~20°15′23″, E110°04′57″~110°19′46″). The study area and its surrounding areas include important villages, aquaculture areas, ports, shipping routes, artificial islands, river estuaries, city centers, and industrial parks on both sides of the strait.
On the north side of the study area, there are many villages with a population of approximately 0.6 million, coastal aquaculture fish ponds, port docks, and a few sand fields. The southern city of Haikou has a higher degree of urbanization. In addition to ports, it mostly consists of residential areas and industrial parks. Notably, there is an artificial island on the south side of the central part of the study area, and the southeast side is the estuary of the Nandu River, the largest river on Hainan Island. The central and western areas of the study area are important shipping routes.

2.2. Sample Collection

We investigated the water quality and plankton communities in the research area in the fall of 2015 (28 November to 5 December) and the spring of 2016 (10 to 19 April).
The water quality survey included basic physicochemical parameters and important nutrient salt content, specifically the temperature, salinity, and pH, COD, DO, NO2-N, NO3-N, NH4-N, DIN (sum of NO2-N, NO3-N, NH4-N), PO4-P, and suspended solid (SS) levels at each sampling point. The study area had 20 evenly distributed water quality sampling points, as shown in Figure 1. Each sampling point was sampled at least three times. We tested the water samples from the bottom layer to the surface, and the average value of all data was the average value from the bottom layer to the surface. No water stratification occurred at any sampling point during the survey. The measurement methods of the water quality parameters all followed the national standard methods [11,12].
The collection of phytoplankton samples was carried out at the same time as the investigation. The coordinates of 12 sampling sites for the phytoplankton survey are shown in Table S1. Biota samples were collected and fixed in the field with a 5% formaldehyde solution. The phytoplankton samples were collected vertically from 1.0 m above the seabed to the surface using a standard Shallow III plankton net at the sampling sites. Quantification and identification of all samples were performed using an XSD-9 microscope (Shanghai Optical Instrument Factory, Shanghai, China), following “The specialties for oceanography survey (GB 12763-2007, China)” [13].

2.3. Data Analysis

The Shannon—Wiener index (H) and dominance of phytoplankton (Y) were calculated using Excel with the following formulas:
H = − ∑ i = 1 s P i log 2 ⁡ P i
Y = n i N × f i
Species with a dominance index greater than or equal to 0.02 were considered dominant species in the community and were included in subsequent analyses. One-way ANOVA and Tukey’s HSD were used to test the significance of differences in water quality parameters, phytoplankton diversity, species number, and abundance between seasons and stations. Differences were considered significant at p < 0.05. The variance analysis was performed using SPSSAU.
Cluster analysis (CA) was used to analyze the similarity of the water quality at all survey sites in the study area in different seasons. PCA was used to further identify the influence of seasonal differences on water quality factors. The CA and PCA were performed using the R language. ArcMap 10.7 was used to present the specific distributions of the water quality parameters and phytoplankton abundance and diversity in the study area. Because the water quality parameters and phytoplankton sampling points were partially different, we used inverse distance weighting to extract the water quality parameters of the phytoplankton sampling points for subsequent analysis [14]. RDA was used to determine the relationship between the water quality parameters and phytoplankton communities in the study area. DCA was performed prior to RDA to determine whether the method was applicable. We included only dominant species in the RDA. Before performing the PCA and RDA analyses, all water quality parameters and phytoplankton abundance were standardized using log10(N + 1). The RDA analyses were performed using CANOCO 4.5.

3. Results

3.1. Differences in Water Quality between the Two Seasons

The mean and variance in the water quality parameters between the two seasons are presented in Table 1. Among the tested water quality parameters, only the NO2-N, NO3-N, and PO4-P levels showed no significant difference between the two survey seasons (p > 0.05, one-way ANOVA).
In autumn, the temperature and COD (0.56 ± 0.21 mg/L), DO (7.75 ± 0.18 mg/L), and SS (26.40 ± 33.17 mg/L) levels were significantly higher than those in spring, while other parameters, such as salinity (31.17 ± 0.21) and pH (8.24 ± 0.02), NH4-N (4.66 ± 0.97 μmol/L), and DIN (6.22 ± 0.86 μmol/L) levels, were higher in spring. It was evident that the significant increase in DIN content was due to the significant increase in NH4-N level in spring (p < 0.05, one-way ANOVA).
PCA and CA analyses were used to detect the similarities between samples. The results showed that a good distinction was formed between spring and autumn in the PCA, with the contribution values of PC1 and PC2 exceeding 90% (Figure 2).
In the CA analysis, in addition to the obvious seasonal distinction, certain differences were also present between different sites within the same season. For example, in autumn, except for S2 and S17, other sites were divided into two groups: one was mainly distributed in the middle of the research area, and the other was mostly located at survey sites closer to the shore (Figure 3).
The clustering results for the spring sites were simpler than those for autumn, mostly divided into two types, with the sites within the research area being almost diagonally classified.

3.2. The Spatial and Temporal Distribution of Water Quality

The distribution of water quality factors with significant differences is shown in Figure 4. During the study period, the average water temperature in the study area ranged from 21.75 °C to 26.50 °C, among which the average water temperature in autumn was higher and more evenly distributed, and the water temperature in the northern coastal survey site was relatively low. Although the average water temperature in spring was lower, the south side temperature was generally higher and the north side temperature was lower (except S5), and the station with the highest temperature appeared at S12 in spring. The salinity varied from 28.98 psu to 31.71 psu in autumn and spring, with the lowest salinity appearing at S13 in autumn and the highest salinity appearing at S20 in autumn. In autumn, the salinity tended to be higher in the south and lower in the north. In spring, the salinity distribution trend was generally more uniform, and the salinity in the nearshore area was relatively low. The pH also fluctuated very weakly, becoming generally weakly alkaline during the study period (8.06–8.27). The COD distribution in autumn and spring was similar, and the nearshore area was higher. Notably, the COD content was higher on the southwest side in autumn. The DO level gradually decreased from northeast to southwest in autumn, and the DO content was higher in the east and northwest of the study area in spring. The distribution of DIN is mainly determined by NH4-N, which was more evenly distributed in autumn and higher in the northeast and central regions of the study area in spring. In terms of the SS level, S2 and S17 were especially high only in autumn, reaching 119.67 and 121.35 mg/L, respectively. The distribution of water quality parameters indicates that the main source of nutrient salts in the study area was the east side of the strait.

3.3. The Phytoplankton Community between the Two Seasons

In this study, 118 species of phytoplankton were identified in 5 phyla and 43 genera, and their relative abundances are shown in Figure 5. There were 79 and 86 species of phytoplankton found in autumn and spring, respectively, of which 47 species were common to the two seasons. ANOVA showed that there were significant differences in the number of phyla, families, genera, and species of phytoplankton between the two seasons, and there were also significant differences in phytoplankton abundance and diversity, which were significantly higher in spring than in autumn; only the uniformity of phytoplankton parameters showed no significant difference (Table 2). The relative abundance of phytoplankton species is shown in Figure 5.

3.4. The Community Composition and Dominant Species in the Two Seasons

Evidently, diatoms had the highest relative abundance in both time and space. Dinoflagellates were present at all survey points, and between all quarters, their relative abundance was second only to that of diatoms. Despite the lower relative abundance of cyanobacteria, they were distributed at most survey points, especially in the spring, where they appeared at all survey points. Golden algae and yellow algae only appeared in the autumn and spring, respectively, with golden algae appearing only in the spring at two sites (Y4 and Y10), while the distribution of yellow algae was more widespread, except Y6 and Y8 (Figure 6). Yellow algae were found at all other sites. Seasonal changes only resulted in a significant increase in the relative abundance of yellow algae.
At the genus level, Chaetoceros and Ceratium were distributed across all survey sites in the two seasons. In spring, the top five genera were Chaetoceros (24.12%), Skeletonema (19.26%), Nitzschia (11.17%), Bacteriastrum (5.51%), and Biddulphia (5.04%), which contributed to over 65% of the relative abundance. In autumn, the top five genera were Chaetoceros (34.78%), Skeletonema (15.72%), Nitzschia (7.09%), Ceratium (4.84%), and Thalassiothrix (4.71%). Additionally, there were significant differences between different seasons for 12 genera, including Ditylum, Thalassionema, Tribonema, Stephanopyxis, Oscillatoria, Dinophysis, Planktoniella, Biddulphia, Spirulina, Microcystis, Lauderia, and Lithodesmium, but most of them appeared in only a particular season. For example, Thalassionema, Tribonema, Stephanopyxis, Planktoniella, Spirulina, and Microcystis appeared only at the spring survey points, Lauderia and Lithodesmium appeared in autumn but disappeared in spring, and Biddulphia appeared in both seasons, but its relative abundance significantly decreased in spring.
In autumn and spring, there were nine dominant species, all of which were diatoms. Among them, Skeletonema costatum and Chaetoceros curvisetus were the common dominant species in both seasons (Table 3). Apart from Cerataulina compacta, which appeared only as a dominant species in autumn, the remaining species were distributed in both seasons, with differences only in the dominance index.

3.5. The Distribution of Diversity and Abundance

In the spring, higher phytoplankton abundance was mostly distributed in the northeast and southwest regions. The biodiversity could be clearly seen to be higher on the western side of the study area in autumn, while the distribution in spring was higher in the nearshore areas and lower in the central regions (Figure 7).

3.6. The Relationship between Environmental Factors and Biodiversity of Phytoplankton

The RDA in this study covered the nine dominant species at 12 survey sites across two seasons and eight environmental factors, with significant differences between the seasons (Figure 8). The RDA provided further insight into the relationship between phytoplankton communities and environmental parameters. The first two axes explained 42.3% and 27.0% of the cumulative variance in the relationship of species–environmental variables, and the eigenvalues were 0.160 and 0.102, respectively. The values of the species–environment correlations for axis 1 and axis 2 were 0.869 and 0.784, respectively, indicating that there was a significant positive correlation between them (Table S2).
The RDA results indicated that the structure of the phytoplankton community in the study area was affected by environmental factors on both temporal and spatial scales. As seen from the figure, the autumn phytoplankton community was mostly influenced by temperature and dissolved oxygen content, and the change from autumn to the following spring was mostly driven by changes in salinity, pH, and nitrogen content.
The RDA results revealed the following relationships between the dominant species of phytoplankton and environmental factors: Chaetoceros curvisetus and Nitzschia pungens, which were most affected by salinity, were mainly distributed on the northeast side of the study area in the summer. Chaetoceros affinis, which was most affected by NH4-N level, was mainly distributed on the western side of the study area in the summer. Skeletonema costatum and Thalassiothrix frauenfeldii were strongly influenced by pH. Cerataulina compacta and Bacteriastrum hyalinum were closely associated with COD level. Chaetoceros lorenzianus and Nitzschia delicatissima positively correlated with SS level and negatively correlated with salinity and nutrient levels.

4. Discussion

4.1. Hydrodynamic Factors and Artificial Activities Impacting Water Quality

Environmental factors in the ocean are easily influenced by various factors, such as weather conditions, hydrodynamics, surface runoff, and human activities [15,16,17]. In this study, the higher water temperature in autumn and higher salinity in spring were consistent with the results of similar area studies, whether from field investigation results or model simulation studies [18,19]. Some studies have used decades of tidal monitoring data to demonstrate that the ocean current in the Qiongzhou Strait flows mainly from east to west throughout the year [2,20]. This indicates that the environmental factors of the Qiongzhou Strait, as a connecting channel between the northern part of the South China Sea and Beibu Gulf, are more likely to be affected by the seawater on the east side of the strait. Numerous studies have shown that the main seawater source on the east side of the strait can be divided into two parts: one part is seawater along the coast of Guangdong, and the other part is seawater from the basin of the South China Sea [4,8,20,21]. Wang’s research shows that the salinity of the seawater along the coast of Guangdong is easily affected by strong surface runoff, that is, spatiotemporal effects [22]. The salinity of the westward adjacent area showed the same profile in Xu’s study [23]. From May, the salinity gradually decreases under the influence of increasing surface runoff, while the salinity of the seawater from the South China Sea basin is usually more stable [1]. This might be the reason for the decrease in salinity from north to south and from east to west in this study. In addition, the surface runoff caused by the stronger precipitation in surrounding areas should not be ignored, such as several rivers on the east side of the Leizhou Peninsula and the Nandu River in the northeastern part of Hainan Island, where the surface runoff is consistent with other rivers along the coast of Guangdong.
The research area is in the middle of the Qiongzhou Strait. Pan’s simulation results of the average DIN model and other water quality parameters in the South China Sea from 2011 to 2015 are consistent with the distribution of DIN in this study [18]. The DIN content on the east side of the strait decreases from east to west and from north to south. In terms of time distribution, different research years, specific sampling times, and analysis methods, such as field surveys and model stimulation, may all cause biases in the time distribution of DIN, such as the opposite results of previous research and this study [18,24,25]. It is very difficult to explain which are more reliable between digital models and field investigations, especially in the complex and changeable Qiongzhou Strait. Our results showed that the DIN content in the Qiongzhou Strait is higher in spring, which is similar to the research conclusions of Wei and others, where the DIN content in spring was higher than that in autumn [24]. A few studies discovered this time distribution rule for DIN in the surrounding sea area of Hainan Island [23,25,26]. In this study, the main component of DIN was NH4-N, which may come from the large amount of aquaculture activities along the north coast of the strait [27]. In addition, the pollutants from coastal Guangdong might also contribute to this phenomenon, along with increasing DIN levels in marine ecosystems [28]. Moreover, Lao et al. indicated that the nutrients through the Qiongzhou Strait to the Beibu Gulf mainly come from coastal Guangdong [29].
After RDA, the dissolved oxygen level in the research area was negatively correlated with DIN content, which was consistent with the research results in the same area [25]. The spatial distribution of DO is also consistent with the distribution of multiyear averages [30], although different seasons will cause significant differences in the average level of dissolved oxygen, but overall, it presents a trend of higher in the north and lower in the south. COD has a higher concentration in coastal areas, which is likely to be closely related to human activities on both sides of the research area, such as a large amount of aquaculture in Xuwen County along the coast and many residential areas distributed in Haikou City on the south side. Furthermore, most of the aquaculture wastewater in these areas is difficult to treat effectively [31]. Our results showed that for the environmental factors in the research area, overall, the environmental factors that depend on water dynamics mainly present a high trend in the east and low in the west. Nutrients and pollutants are closely related to human activities on both sides of the strait.

4.2. Vital Environmental Factors Induced Differences in the Phytoplankton Community

In this study, the abundance and diversity of phytoplankton communities showed significant variations across different seasons. However, it was consistently dominated by diatoms, with a higher presence in spring than in autumn. This phenomenon was reflected by the Chla content and was consistent with the research findings by Wang et al. [32], as illustrated in Figure 6. At the level of cell density or abundance, for the diatoms and dinoflagellates that constitute the major abundance, a similar trend of higher presence in spring and lower in autumn was also observed in the nearby sea areas of the study region [23]. Despite limited studies on how environmental factors influence the abundance and diversity of phytoplankton communities in the research area, we still expected to identify a series of potential influencing factors affecting the abundance and biodiversity of phytoplankton in the study area using Spearman’s correlation analysis (Table S3).
Regarding the abundance of phytoplankton, all environmental factors showing significant differences in different quarters were significantly correlated with important indicators of phytoplankton communities (abundance and diversity). In this study, it was clear that pH, salinity, and DIN (or NH4-N) content were positively correlated with abundance. For aquatic organisms, especially phytoplankton, these are the most fundamental and important environmental factors.
First, pH is a complex factor that can influence phytoplankton growth. It affects phytoplankton by affecting photosynthesis and nutrient absorption. During this study, both the abundance and diversity of phytoplankton increased over time, along with pH values. The pH of the study area remained within the common range suitable for the growth of most phytoplankton and the global ocean pH (8.0–8.2) in two seasons, both of which are weakly alkaline [33]. In addition to pH influencing phytoplankton growth and reproduction, growing phytoplankton also have a regulatory effect on pH values; for example, as phytoplankton density increases, the demand for CO2 also increases, leading to an increase in environmental pH. Therefore, the results of this study confirmed the complex interaction between pH and phytoplankton in the study area. While the pH of the study area is conducive to the growth of marine phytoplankton, whether a slight increase in pH is the main cause of the increase in phytoplankton abundance requires further research.
Many studies have shown that salinity can be a crucial factor driving the growth and succession of marine phytoplankton communities [34]. We found similar results: as the salinity increased, the abundance of phytoplankton also increased. Notably, salinity is essential in affecting phytoplankton communities in areas usually located at river mouths, inland waters, and coasts, where there are significant temporal and spatial differences in salinity.
As one of the most important factors, water temperature can control the seasonal dynamics of phytoplankton succession [35,36]. Regarding temperature, it is a common understanding that diatoms can grow better at lower temperatures [37]. Thus, it is not surprising that lower phytoplankton abundance occurs during autumn with a higher water temperature.
The abundance of phytoplankton communities is generally considered to be positively correlated with the dissolved oxygen content in the environment. However, in our study, we found that the phytoplankton abundance and diversity were negatively correlated with the dissolved oxygen content. This phenomenon appeared along almost the entire coastal area in China, which indicated that the phytoplankton abundance and species numbers were negatively correlated with the DO content (higher than 5.0 mg L−1) [38].
In addition to the influence of the above environmental factors on phytoplankton, nutrients are also essential limiting factors for phytoplankton growth. In this study, apart from NH4-N and DIN, there were no significant differences in other major nutrients, so we have discussed and analyzed the impact of NH4-N and DIN on the abundance of phytoplankton in the study area. Furthermore, the DIN content in this study was mainly affected by NH4-N changes and showed a correlation, so the impact of nutrients on the phytoplankton was based on the variation in NH4-N. As a nitrogen source, diatoms can easily utilize NH4-N [39]. The significant increase in NH4-N in spring is likely an important factor for the rise in phytoplankton abundance [40]. From a spatial distribution perspective, in areas with higher phytoplankton abundance in spring, the content of NH4-N is relatively lower. This phenomenon was more evident on the south side of the study area. The occurrence of this phenomenon is not only due to the main source of NH4-N coming from the north of the strait and the eastward area but also due to the efficient utilization of NH4-N by phytoplankton, which cannot be easily overlooked. In addition to nitrogen sources, phosphates and N/P are also important factors controlling phytoplankton communities [41]. In this study, although there were no seasonal differences in PO4-P, its distribution was similar to that of NH4-N, especially in spring, and their main source was also the north of the strait. Because most of the phytoplankton constituting the community in this study were diatoms, the silicate distribution in the water body is also worth noting, but unfortunately, we did not conduct a silicate survey.
The factors influencing the diversity of phytoplankton are also complex. It is not difficult to see from the results of this study that the environmental factors related to the diversity index have essentially the same correlation with community abundance. Specifically, pH and NH4-N levels show significant positive correlations, while DO content shows a significant negative correlation. Therefore, we infer that when other factors fluctuate less, an increase in NH4-N content is an important factor affecting phytoplankton diversity in the study area. When the nutrient content is high, more species can obtain sufficient material sources. Furthermore, input from terrestrial sources may also be an important reason for the increase in biodiversity. It can be seen from the spatial distribution that the biodiversity in the coastal areas of the straits is higher in spring.
The succession of phytoplankton is largely determined by the interactions and seasonal cycles of chemical (nutrients) and physical (weather) factors [42]. Consequently, the results indicate that basic environmental factors not only influence the abundance and diversity of phytoplankton but also have complex relationships with each other.

4.3. Relationship between Intensively Varied Environmental Factors and Important Dominant Species

As little information has been reported about the specific compositional structure of phytoplankton communities in the Qiongzhou Strait and their associations with environmental factors, the present observations form the baseline for future study in this area of the Qiongzhou Strait.
Although there were significant differences in the abundance and biodiversity of phytoplankton communities between seasons, the dominant species in autumn and spring showed a certain similarity. For instance, the dominant species in the study area during both seasons were diatoms, with no significant difference in the relative abundance of most dominant species between the two seasons. Only the relative abundance of Chaetoceros affinis showed significant differences between seasons. Among all the dominant species, common phytoplankton in the coastal areas of China and some diatoms that can form algal blooms are not uncommon [43]. Despite the enormous variation in phytoplankton abundance, Skeletonema costatum and Chaetoceros curvisetus remain dominant species in both autumn and spring.
Although we included both dominant species and limiting factors in the RDA, the analysis results also showed the responses of different dominant species to environmental factors. For example, Skeletonema costatum, as the most common phytoplankton along the coast of China, is a dominant species across multiple sea areas and timescales and forms red tides due to the increase in DIN content, especially for small-celled algae [44]. These findings are consistent with our results. Skeletonema costatum and Chaetoceros curvisetus were the key species and were mostly distributed in the southwest and northeast in spring. In the RDA, they were closely associated with higher NH4-N levels and salinity, respectively, suggesting that they prefer different environmental conditions in the Qiongzhou Strait during the study period.
Other studies have also confirmed that NH4-N can regulate phytoplankton distribution, including the genus Thalassiothrix, as well as Chaetoceros affinis’s growth [45,46]. Species with increased relative abundance in the spring seem to prefer a more nutrient-rich water environment. Therefore, in this study, salinity and DIN content, especially NH4-N content, are important factors driving the succession of phytoplankton communities.

5. Conclusions

The distribution and contents of the water quality and phytoplankton community in the Qiongzhou Strait were investigated in autumn and spring. Using one-way, PCA, CA and RDA analyses, we found that the temperature, salinity, DO content, and SS content were higher in autumn. The highest values of these parameters often appeared in the coastal area in the strait. The nutrient levels showed nonsignificant differences between the two seasons, except for NH4-N, which was much higher in spring. These differences resulted in heterogeneity in autumn and spring in the Qiongzhou Strait during the study period. Over time, the number of species, biodiversity, and abundance of the phytoplankton community increased, while diatoms still dominated in both seasons. Chrysophyta and Xanthophyta appeared only in autumn and spring, respectively. The dominant species were all diatoms, and more dominant species were found in autumn. Artificial activity and nutrient import along both sides of the strait caused changes in environmental factors and finally affected the structure and distribution of the phytoplankton community.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/w15213792/s1. Table S1: The coordinates of biota samples. Table S2: Eigenvalues for RDA axes, results related to species-environment correlations, variation and cumulative % of species data and species-environment relation. Table S3: Coefficient of Spearman correlation between water quality and phytoplankton community.

Author Contributions

Conceptualization, Y.M. and Q.C.; data curation, Y.M.; formal analysis, C.X.; funding acquisition, Y.M.; investigation, H.Z.; methodology, C.X.; project administration, Y.M.; resources, C.X.; software, X.L.; supervision, Y.M.; validation, X.L.; visualization, H.Z.; writing—original draft, C.X.; writing—review and editing, Q.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Open Research Fund of the State Key Laboratory of Estuarine and Coastal Research (Grant number SKLEC-KF202310).

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

The authors also thank the anonymous reviewers for their helpful comments and suggestions.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. The distribution of sampling sites in the study area.
Figure 1. The distribution of sampling sites in the study area.
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Figure 2. PCA on transformed environmental parameters of all sampling stations during autumn and spring.
Figure 2. PCA on transformed environmental parameters of all sampling stations during autumn and spring.
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Figure 3. Results of the cluster analysis based on environmental parameters during spring and summer.
Figure 3. Results of the cluster analysis based on environmental parameters during spring and summer.
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Figure 4. Heatmap of spatial and temporal distribution of water quality in autumn and spring. The capital letters (A–H) represent the environmental parameters in autumn and lowercase letters (a–h) represent them in spring. (A): Temperature; (B): salinity; (C): pH; (D): COD level; (E): DO level; (F): NH4-N level; (G): SS level; (H): DIN content.
Figure 4. Heatmap of spatial and temporal distribution of water quality in autumn and spring. The capital letters (A–H) represent the environmental parameters in autumn and lowercase letters (a–h) represent them in spring. (A): Temperature; (B): salinity; (C): pH; (D): COD level; (E): DO level; (F): NH4-N level; (G): SS level; (H): DIN content.
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Figure 5. The number of species between two seasons.
Figure 5. The number of species between two seasons.
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Figure 6. The composition of the phytoplankton structure at phylum and genus levels. The letters (A,B) represent the relative abundance of phyla and genera levels for phytoplankton, respectively. The letters in red on the “Y-axis” represent sampling sites in spring and the ones in black represent them in autumn. The figure legends under figures interpret the phylum and the legends on the right side of them are the legends for the genera.
Figure 6. The composition of the phytoplankton structure at phylum and genus levels. The letters (A,B) represent the relative abundance of phyla and genera levels for phytoplankton, respectively. The letters in red on the “Y-axis” represent sampling sites in spring and the ones in black represent them in autumn. The figure legends under figures interpret the phylum and the legends on the right side of them are the legends for the genera.
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Figure 7. Heatmap of the spatial and temporal distributions of the abundance and diversity of phytoplankton. The letters (A,B) represent the abundance of phytoplankton in autumn and spring, respectively, while (C,D) represent the biodiversity index.
Figure 7. Heatmap of the spatial and temporal distributions of the abundance and diversity of phytoplankton. The letters (A,B) represent the abundance of phytoplankton in autumn and spring, respectively, while (C,D) represent the biodiversity index.
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Figure 8. RDA of the relationship between the environmental variables and phytoplankton taxa. The red arrow with dotted line represented the environmental factors and the deep blue ones represented dominant species.
Figure 8. RDA of the relationship between the environmental variables and phytoplankton taxa. The red arrow with dotted line represented the environmental factors and the deep blue ones represented dominant species.
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Table 1. The average environmental factors of water quality in autumn and spring.
Table 1. The average environmental factors of water quality in autumn and spring.
ParameterSeason (Mean ± Std)p
Autumn (n = 20)Spring (n = 20)
Temperature (°C)25.75 ± 0.4223.23 ± 1.190.000 **
salinity30.31 ± 0.6731.17 ± 0.210.000 **
pH8.09 ± 0.028.24 ± 0.020.000 **
COD (mg/L)0.56 ± 0.210.38 ± 0.090.001 **
DO (mg/L)7.75 ± 0.186.98 ± 0.210.000 **
NO2-N (μmol/L)0.24 ± 0.110.26 ± 0.060.367
NH4-N (μmol/L)0.51 ± 0.304.66 ± 0.970.000 **
NO3-N (μmol/L)1.00 ± 0.381.30 ± 0.590.059
DIN (μmol/L)1.75 ± 0.566.22 ± 0.860.000 **
PO4-P (μmol/L)0.01 ± 0.010.02 ± 0.020.075
SS (mg/L)26.40 ± 33.176.91 ± 5.830.014 *
Note: * and ** means p < 0.05 and p < 0.01, respectively.
Table 2. The biodiversity parameters of phytoplankton in two seasons.
Table 2. The biodiversity parameters of phytoplankton in two seasons.
ItemsSeason (Mean ± Std)p
Autumn (n = 12)Spring (n = 12)
Phylum3.00 ± 0.433.83 ± 0.390.000 **
Family10.08 ± 1.4413.42 ± 1.440.000 **
Genus15.83 ± 2.6919.08 ± 1.830.002 **
Species numbers22.92 ± 3.0627.75 ± 2.340.000 **
Abundance (×104 cells/m3)79.90 ± 19.38168.57 ± 23.080.000 **
Diversity3.28 ± 0.243.49 ± 0.170.021 *
Evenness0.73 ± 0.050.72 ± 0.030.831
Note: * and ** means p < 0.05 and p < 0.01, respectively.
Table 3. The dominant species and their relative abundances in the two seasons.
Table 3. The dominant species and their relative abundances in the two seasons.
Dominant SpeciesAutumnRA (%)SpringRA (%)
Skeletonema costatum0.09 *0.19 ± 0.220.11 *0.16 ± 0.13
Chaetoceros curvisetus0.06 *0.11 ± 0.130.08 *0.12 ± 0.13
Chaetoceros lorenzianus0.04 *0.07 ± 0.130.010.02 ± 0.06
Chaetoceros affinis (p < 0.05)0.010.01 ± 0.030.07 *0.12 ± 0.13
Nitzschia delicatissima0.03 *0.05 ± 0.080.000.01 ± 0.01
Nitzschia pungens0.010.01 ± 0.020.03 *0.05 ± 0.08
Bacteriastrum hyalinum0.03 *0.05 ± 0.100.000.01 ± 0.01
Cerataulina compacta0.02 *0.04 ± 0.09//
Thalassiothrix frauenfeldii0.000.01 ± 0.020.03 *0.05 ± 0.08
Notes: * means dominant species in each season. RA means the relative abundance (%). “/" means species was not found in all samples.
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Xu, C.; Ma, Y.; Zhang, H.; Li, X.; Chen, Q. The Impact of Environmental Factors on the Spatiotemporal Heterogeneity of Phytoplankton Community Structure and Biodiversity in the Qiongzhou Strait. Water 2023, 15, 3792. https://doi.org/10.3390/w15213792

AMA Style

Xu C, Ma Y, Zhang H, Li X, Chen Q. The Impact of Environmental Factors on the Spatiotemporal Heterogeneity of Phytoplankton Community Structure and Biodiversity in the Qiongzhou Strait. Water. 2023; 15(21):3792. https://doi.org/10.3390/w15213792

Chicago/Turabian Style

Xu, Chunling, Yu Ma, Hao Zhang, Xiaoming Li, and Qi Chen. 2023. "The Impact of Environmental Factors on the Spatiotemporal Heterogeneity of Phytoplankton Community Structure and Biodiversity in the Qiongzhou Strait" Water 15, no. 21: 3792. https://doi.org/10.3390/w15213792

APA Style

Xu, C., Ma, Y., Zhang, H., Li, X., & Chen, Q. (2023). The Impact of Environmental Factors on the Spatiotemporal Heterogeneity of Phytoplankton Community Structure and Biodiversity in the Qiongzhou Strait. Water, 15(21), 3792. https://doi.org/10.3390/w15213792

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