Spatial and seasonal variations in the planktonic ciliate community and its relationship with environmental factors in Daya Bay, the South China Sea

Twelve sampling sites were located in four areas of Daya Bay where different types of human activities occur (Fig. 1). Sampling sites 1-3 were located in the Dapeng Cove aquaculture area (DC), sites 4-6 near the Daya Bay Nuclear Power Station (DNPS), sites 7-9 at artificial reefs (ARs), and sites 10-12 near the water outlets of the Pinghai fossil fuel-fired Power Station (PFPS).

Figure 1

Planktonic ciliate samples were collected with a Niskin water sampler at a depth of 0.5 m from Daya Bay in January, April, August and November 2014, representing the temporal ciliate community in winter, spring, summer and autumn, respectively. Seawater samples of 2500 ml (fixed with 1% formaldehyde in plastic bottles) were used for quantitative analysis and identification of ciliates. A total of 48 ciliate samples were fixed and analyzed. Environmental factors, such as pH, salinity (Sal.), and temperature (Temp. ^oC) were measured in situ at sampling sites, using a multi-parameter sensor (YSI Professional Plus, USA). Dissolved oxygen concentration (DO, mg l^-1), nitrate nitrogen (NO₃^-, μmol l^-1), nitrite nitrogen (NO₂^-, μmol l^-1), ammonium nitrogen (NH₄⁺, μmol l^-1) and soluble reactive phosphate (PO₄^3-, μmol l^-1) were determined using a UV-visible spectrophotometer (UV2450, Shimadzu) according to marine monitoring specifications (GB 17378.4-2007, 2007). DIN is the sum of NO₃^-, NO₂^-, and NH₄⁺. Chlorophyll-a (Chla, μg l^-1) concentrations were measured using a Fluor-meter (Tuner-10-AU).

Formaldehyde fixed samples were settled for at least 48 h and then concentrated to 50 ml (Utermöhl 1958). Each time, 0.1 ml of a well-mixed concentrated sample was placed on a microscope slide and ciliates were counted under a light microscope at 200× or 400×. Ten slide replicates were examined for each concentrated sample. Tintinnids were identified on the basis of lorica morphology and species description provided by Kofoid and Campbell (1929, 1939) and Lynn (2008). Other ciliates were identified following Song et al. (2008). The taxonomic scheme mainly refers to Lynn (2008). All ciliates were finally identified to the lowest possible taxa.

The number of species and ciliate abundance were calculated for each sample. The dominance (Y) in samples was calculated according to the following formulae:

Y=(niN)×fi $$Y = \left( {{{{n_i}} \over N}} \right) \times {f_i}$$

where n_i is the abundance of the i^th species, N is the total number of individuals, f_i is the occurrence frequency of the ith species. The criterion for dominant species was Y ≥ 0.02.

Cluster analysis from the Primer v5.0 software package was used to investigate the ciliate community structure (Clarke and Gorley 2001). Bray-Curtis similarity matrices were computed on the fourth-root transformed ciliate abundance data and the Euclidean distance matrix was computed on log(x+1)-transformed environmental data (including Chla). The group-average clustering was used to divide ciliate communities into several assemblages. Differences between groups of samples were tested by the routine ANOSIM (global R-values range from 0 to 1, and a higher global R value demonstrates more significant variance). Sample relationships based on a comparison of similarity matrices were displayed using multi-dimensional scaling (MDS). BIOENV (biota and/or environment matching, using Spearman rank correlations) was used to explore the potential relationships between environmental parameters and biotic data.

Detailed relationships between ciliate communities and environmental factors were analyzed by CANOCO 4.5 (ter Braak and Smilauer 2002). To prevent any disproportionate influence of rare species in the subsequent analysis, only species with incidence >10% in all samples (or total sites) and abundance >1.0% of the total ciliate abundance were considered. First, detrended correspondence analysis (DCA) was employed on the ciliate abundance data to decide whether linear or unimodal ordination methods should be applied. If the length of the gradient was greater than 3, Canonical Correspondence Analysis (CCA) was used, otherwise Redundancy Analysis (RDA) was applied. CCA or RDA analysis was conducted based on log(x+1)-transformed species abundance and environmental data (except for pH). In addition, Spearman correlation analysis between ciliate communities and environmental factors was computed by the SPSS v20.0 statistical program.

Mean values of ten environmental factors were summarized in Table 1. Water temperature followed a clear seasonal pattern, ranging from 16.93^oC to 27.75^oC (mean 23.58^oC), and the maximum was recorded in DNPS areas in four seasons. Salinity peaked in summer (33.94 PSU). Values of pH ranged from 8.07 to 8.27, on average 8.21. Values of DO varied inversely with temperature, and the maximum was recorded in winter. Concentration of NO₂^-, NH₄⁺, NO₃^- and DIN peaked in summer, and was relatively low in winter. Soluble reactive phosphate (PO₄^3-) ranged from 0.20 μmol l^-1 to 0.41 μmol l^-1, which was relatively high in summer. Chla concentration peaked in summer (4.42 μg l^-1), but decreased in winter (1.78 μg l^-1).

Table 1

Environmental variables for Daya Bay water samplings in winter 2013, spring, summer and autumn 2014

The taxonomic composition of ciliate communities observed during the study period is summarized in Table 2. A total of 41 planktonic ciliate species representing 22 genera and eight orders (Tintinnida, Oligotrichida, Euplotida, Cyclotrichida, Haptorida, Pleurostomatida, Prostomatida, Pleuronematida) were recorded during the one-year survey. Tintinnida and Oligotrichida were represented by the highest number of species, accounting for 70% and 15% of the total number of species, respectively (Fig. 2a). However, Oligotrichida was the most abundant order and accounted for up to 54.35% of the total ciliate abundance, followed by Tintinnida and Cyclotrichida (Fig. 2b).

Table 2

List of ciliate species encountered in Daya Bay

Figure 2

The dominant species (dominance Y ≥ 0.02) in each season are listed in Table 3. Ciliate communities were dominated by Tintinnopsis minuta, Strombidium globosaneum, Tintinnopsis beroidea and Strombidium conicum in winter. S. conicum and S. globosaneum were still dominant in spring, accompanied by two new dominant species – Spirotontonia turbinata and Laboea strobila. In summer, five new dominant species, Amphorellopsis acuta, Eutintinnus stramentus, Tintinnopsis tocantinencis, Tintinnopsis corniger, Helicostomella longa, occurred along with S. conicum, S. globosaneum and S. turbinata. Additionally, H. longa was the most dominant species in summer. In autumn, the dominant species from summer were partially replaced by new species, i.e. T. minuta, Tintinnopsis nucula, Wangiella dicollaria and Mesodinium rubrum. S. conicum and S. globosaneum were still the most dominant species in autumn.

Table 3

Dominant ciliate species in each season in Daya Bay (species dominance, Y ≥ 0.02)

The total number of ciliate species ranged from 6 to 32 (Fig. 3a). The smallest number of species was observed in spring, and the highest one in summer. In terms of spatial distribution, the maximum number of species was recorded at ARs and the minimum at DC (Fig. 3a).

Figure 3

The abundance of ciliates showed a marked seasonal change, with the highest values observed in spring and the lowest in summer (Fig. 3b). In spring, their abundance ranged from 350 to 3120 ind. l^-1, with an average of 1409.1 ± 92.6 ind. l^-1. Interestingly, S. conicum and S. turbinata were primarily responsible for the peak, reaching the abundance of 7750 ind. l^-1 and 6220 ind. l^-1, respectively. In terms of spatial distribution, higher abundance of ciliates was recorded in the DC area, whereas lower values were determined in the DNPS area.

Cluster analysis divided the ciliate communities into four groups at a similarity level of 40% in terms of their temporal distribution in Daya Bay during the study period (Fig. 4). Group 1 comprised some of the samples collected in autumn. Group 2 included all winter samples, while Group 3 consisted of all spring samples except for the sample collected from site 3 in autumn. Group 4 contained all the remaining autumn and all summer samples. The multidimensional scaling test (MDS) produced a similar result as mentioned above (Fig. 5). ANOSIM demonstrated that there were significant differences between each group (global R = 0.836, p < 0.001), implying an obvious seasonal pattern of ciliate communities. Although the interrelationship within each group differed, the difference between groups were more obvious than within groups, thus seasonal variation in ciliate communities was more significant than the spatial variation.

Figure 4

Figure 5

BIOENV analysis revealed that ciliate communities were significantly correlated with abiotic environmental factors, i.e. temperature, salinity, NO₂^-, NO₃- and PO₄^3- (Table 4). Spearman correlation analysis demonstrated that ciliate abundance was significantly positively correlated with pH and DO, and negatively correlated with water salinity (Table 5). Similarly, there was a significant negative correlation between ciliate abundance and salinity in spring. In summer, temperature, DO, NO₂^- and PO₄^3- were the main factors affecting the ciliate abundance, whereas NH₄⁺ was significantly positively correlated with ciliate abundance in autumn. As can be observed in the CCA plot of four seasons (Fig 6a), samples collected in winter were generally gathered on positive Axis 2, indicating higher optima for DO, NO₃^-, PO₄^3-, NO₂^- and DIN, and lower values for temperature and pH. Samples collected in summer were entirely on positive Axis 1, indicating higher optima for temperature and NH₄⁺, whereas lower optima for pH, NO₃^-, DIN and DO. Spring and autumn samples were located on negative Axis 2, indicating higher optimum values for temperature and pH, but lower values for PO₄^3-, NO₃^-, NO₂^-, DO and DIN. Results of RDA implied that environmental variables significantly affecting ciliate communities during four seasons were different. Environmental variables that significantly affected the spatial pattern of ciliate communities in winter were DO, Chla, NO₂^-, NH₄⁺, PO₄^3- and salinity (Fig. 6b). The highest DO concentration was determined at site Wi1 where the ciliate abundance peaked. Similarly, the highest Chla concentration was determined at site Wi10 where ciliate abundance was relatively high. In spring, the significant variables were temperature, PO₄^3-, NO₂^-, salinity and pH. High temperature, PO₄^3-, NO₂^-, and low salinity, pH were determined at site Sp1 and Sp3 (DC area) where ciliate abundance peaked (Fig. 6c). In contrast, the highest values of pH and salinity were determined at site Sp12 (PFPS area), where the ciliate abundance and species number were the lowest. The most significant environmental variables in summer were NO₃^-, NO₂^-, PO₄^3-, NH₄⁺, salinity and DO (Fig. 6d). The levels of NO₃^-, PO₄^3- and NO₂^- were higher at sites Su1, Su2 and Su3, where ciliate abundance was relatively high. Environmental factors significantly affecting the spatial pattern of ciliate communities in autumn were Chla, NO₃^-, NO₂^-, NH₄⁺ and PO₄^3- (Fig. 6e). High levels of NO₃^-, NH₄⁺ and DIN were determined at sites Au7, Au8 and Au9, where ciliate abundance and species number were relatively high. However, lower values of ciliate abundance were recorded at sites Au4, Au5 and Au6, where Chla concentration had the lowest value and temperature – the highest value.

Table 4

Summary of results from biota-environment (BIOENV) analysis, with the top ten correlations corresponding to different variables (R is Spearman correlation coefficient)

Table 5

Correlation between environmental variables and ciliate abundance (N), the number of species (S) in four seasons in Daya Bay

Figure 6

Our study revealed that planktonic ciliate communities exhibited an obvious seasonal and spatial variation in terms of species composition and abundance in Daya Bay. The number of ciliate species was high in summer and autumn, while low in winter and spring. In contrast, the ciliate abundance in spring was significantly higher compared to other three seasons. Similar results were obtained in other marine systems, such as the Bay of Biscay (Urrutxurtu et al. 2003) and the Bay of Baisha (our unpublished data), where one peak of ciliate abundance was observed in spring. However, in the Helgoland Roads (the North Sea), Yang et al. (2015) reported two peaks of ciliate abundance in spring and summer, respectively. Yu et al. (2013) reported two peaks of ciliates in spring and autumn on Zhangzi Island (the northern Yellow Sea). Jiang et al. (2011b) found that the abundance was high in summer and winter, while low in spring and autumn in Jiaozhou Bay. These differences in temporal variation patterns of ciliate communities may result from different locations of the study sites, different sampling seasons or different study methods applied. For example, samples in our study were collected from the surface water (at a depth of 0.5 m), while the water depth of ciliate samples in another study (Yu et al. 2013) was between 32 m and 58 m. In addition, environmental factors in marine regions were different, for example Jiang et al. (2011b) reported three peaks in Chla concentrations: in late August, in mid-January and mid-March. On the other hand, there was one peak in Chla concentration in our study – in summer.

Higher abundance of ciliates occurred in the DC area. Intensive aquaculture activities led to elevated nutrient concentrations (Wang et al. 2009) and phytoplankton biomass (Chla) stays high in the DC area (Song et al. 2004; Li et al. 2014). These primary producers are grazed by higher groups in the trophic chain (e.g. ciliates, zooplankton and shellfish) (Azam et al. 1983; Pitta et al. 2009; Silva et al. 2012). Thus, phytoplankton abundance was positively correlated with ciliate abundance (Wang et al. 2014b). In contrast to DC area, there was a lower ciliate abundance in the DNPS area, which may be directly or indirectly affected by a thermal discharge from DNPS. Thermal effluents can reduce the phytoplankton biomass, abundance and chlorophyll-a (Chuang et al. 2009; Li et al. 2014), which lead to a lack of food resources for ciliate and indirectly affect ciliate abundance. Additionally, thermal effluents can also directly lead to reduced survival of the entrained zooplankton (Hoffmeyer et al. 2005; Kartasheva et al. 2008). Thus, the thermal discharge of DNPS also directly caused a decline in ciliate abundance.

Multivariate correlation analysis demonstrated that seasonal and spatial variation in planktonic ciliate communities were significantly related to environmental variations, especially water temperature, pH, dissolved oxygen, nutrients (e.g. NO₂^-, NO₃^-, NH₄⁺ and PO₄^3-) and Chla. These findings are consistent with previous reports for planktonic ciliate in Jianzhou Bay (Jiang et al. 2011b), Beibu Gulf (Wang et al. 2013; Wang et al. 2014c), and Shenhu Bay (Wang et al. 2014b). Furthermore, we found that the dominant abiotic factors affecting the spatial pattern of ciliate communities varied between seasons. For instance, ciliate abundance was significantly and positively correlated with Chla in winter. It is a well-known fact that water temperature was one of the most important environmental variables affecting the growth and reproduction of aquatic organisms (Langford 1990). Therefore, the phytoplankton growth and abundance were lower during winter. As most ciliates feed on phytoplankton, the phytoplankton abundance (chlorophyll-a concentration) in winter was one of the important factors influencing the community and abundance of ciliates. Consistent with our results, the previous studies indicated that there was a significant and positive correlation between the abundance of ciliates and the abundance of phytoplankton (Wang et al. 2014b; Yang et al. 2015). Therefore, these results suggested that food availability is an important factor influencing the seasonal variation in the ciliate community.

In addition, the BIOENV analyses, spearman correlation analysis and CCA have suggested that ciliate communities in our study were significantly correlated with nutrient levels (e.g. PO₄^3-, NO₂^-, NO₃^-, NH₄⁺). Consistent with our results, previous studies indicated that the total abundance of ciliates was significantly affected by the level of phosphorus and nitrogen (Wang et al. 2013, 2014c). Simultaneously, Jiang et al. (2011b) suggested that the ciliate community variation in Jiaozhou Bay was correlated with NO₃^- and soluble reactive phosphates. Therefore, these findings demonstrated that the nutrient level is also an important factor influencing the seasonal and spatial variations in ciliate abundance. Moreover, other factors such as salinity, DO and water temperature also played an important role in determining both the spatial and seasonal pattern of ciliate communities in Daya Bay.

Furthermore, it is worth noting that the nutrient level and Chla concentration was relatively low in spring. However, the ciliate abundance was the highest in four seasons during the study. This may be related to the ciliate community, especially the ciliate species composition structure. In spring, Oligotrichida was the most abundant order in terms of both the number of species and their abundance. Generally, tintinnids were heterotrophic, whereas oligotrichs were autotrophic or heterotrophic (Tan et al. 2010). Heterotrophs feed on phytoplankton and bacteria, thus low nutrient levels in spring affect the growth of phytoplankton, and then the total abundance of ciliates was indirectly affected (Sanders 1995). By contrast, oligotrich ciliates can absorb nutrients directly rather than rely only on phytoplankton and although Chla concentration was low in spring, the ciliate abundance was still high. Interestingly, we also found that the genus Strombidium, especially Strombidium conicum and Spirotontonia turbinata, predominated in spring, thus the two ciliate species should be taken into account for future ecological studies.