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Study of monthly variations in primary production and their relationships with environmental factors in the Daya Bay based on a general additive model
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Jianhua KANG1, *, Hao HUANG1, Weiwen LI1, Yili LIN1, Xingqun CHEN1
Acta Oceanologica Sinica | 2018, 37(12) : 107 - 117
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Acta Oceanologica Sinica | 2018, 37(12): 107-117
• Marine Biology •
Study of monthly variations in primary production and their relationships with environmental factors in the Daya Bay based on a general additive model
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Jianhua KANG1, *, Hao HUANG1, Weiwen LI1, Yili LIN1, Xingqun CHEN1
Affiliations
  • 1 Laboratory of Marine Biology and Ecology, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China
Published: 2018-12-25 doi: 10.1007/s13131-018-1281-6
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In this study, the horizontal and vertical distribution of primary production (PP) and its monthly variations were described based on field data collected from the Daya Bay in January–December of 2016. The relationships between PP and environmental factors were analyzed using a general additive model (GAM). Significant seasonal differences were observed in the horizontal distribution of PP, while vertical distribution showed a relatively consistent unimodal pattern. The monthly average PP (calculated by carbon) ranged from 48.03 to 390.56 mg/(m2·h), with an annual average of 182.77 mg/(m2·h). The highest PP was observed in May and the lowest in November. Additionally, the overall trend in PP was spring>summer>winter>autumn, and spring PP was approximately three times that of autumn PP. GAM analysis revealed that temperature, bottom salinity, phytoplankton, and photosynthetically active radiation (PAR) had no significant relationships with PP, while longitude, depth, surface salinity, chlorophyll a (Chl a) and transparency were significantly correlated with PP. Overall, the results presented herein indicate that monsoonal changes and terrestrial and offshore water systems have crucial effects on environmental factors that are associated with PP changes.

primary production  /  environmental factors  /  general additive model  /  monthly variations  /  Daya Bay
Jianhua KANG, Hao HUANG, Weiwen LI, Yili LIN, Xingqun CHEN. Study of monthly variations in primary production and their relationships with environmental factors in the Daya Bay based on a general additive model[J]. Acta Oceanologica Sinica, 2018 , 37 (12) : 107 -117 . DOI: 10.1007/s13131-018-1281-6
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1 Introduction
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The Daya Bay, which is located on the eastern side of the Zhujiang Estuary (Pearl River Estuary), is surrounded by the Dapeng Peninsula in Shenzhen City, the southern coast of Huiyang City, and the Pinghai Peninsula in Huidong County, Guangdong Province, China. This bay neighbors Hong Kong to the southwest and is connected to the South China Sea to the south. The system represents the largest mountain drowned valley-type semi-enclosed bay in Guangdong Province, where the Daya Bay Nuclear Power Plant and the Lingao Nuclear Power Plant are located.
Primary production (PP), which is an important carrier of the marine carbon cycle (Falkowski et al., 1998), represents the ability to produce organic matter in the sea and forms a basis for assessing the fishery production potential and maximum sustainable yield, as well as for maintaining the material supply of marine ecosystems (Chassot et al., 2010). The Daya Bay is situated at the confluence of low-salinity waters within the Zhujiang Estuary and high-salinity waters offshore in the South China Sea. Owing to the high PP and abundant sources of food, this water area has become an important site for aquaculture of many economic fish, shellfish, and crustaceans. The area is also an important germplasm bank of subtropical species in China, as well as one of the most important fishery areas in Guangdong Province (Yu et al., 2017).
Since the Daya Bay Nuclear Power Plant started operations in 1994, environmental pollution caused by frequent intensive development and construction without consideration of the carrying capacity of ecosystems has led to marked structural changes and functional degradation of ecosystems in the Daya Bay. This is mainly reflected by the following aspects. (1) Water is developed from the oligotrophic to mesotrophic state, and there is a trend of eutrophication in some areas. The limiting nutrient factor in the Daya Bay shifted from nitrogen in the 1980s to phosphorus (Wang et al., 2004b). (2) The composition of biotic communities is significantly miniaturized, the biological diversity is reduced, and the biological resources are degraded (Liu et al., 2013; Li et al., 2015a). (3) Eutrophication and bottom trawling because of the development of aquaculture have caused changes in the sedimentary environment and overfishing, which have accelerated ecosystem degradation and thus reduced ecosystem stability in the Daya Bay (Xu, 2013; Song et al., 2012). Therefore, marine ecosystems in this region have received a great deal of attention and many studies have been conducted to investigate PP distribution (Song et al., 2004), changes in water quality (Chen et al., 2012), variations in fish catch (Peng et al., 2001), phytoplankton community structure (Liu et al., 2006; Wang et al., 2016), zooplankton species composition (Du et al., 2013), ecosystem structure and function in this sea area (Ke et al., 2009; Mo et al., 2017). Despite extensive studies of different aspects of the ecological environment in the Daya Bay, field measurements of monthly variations of PP throughout the year in this sea area are very rare. Wu et al. (2009) used a neural network prediction model for dynamic prediction and estimation of PP in the Daya Bay. That model predicted PP variations well, but failed to explain the variations of affected variables.
The general additive model (GAM) is a mathematical analysis model that originates from the original data. The GAM has mainly been used to explore the relationship between response variables and independent variables, including linear, nonlinear, and co-existing linear and nonlinear relationships (Wood, 2004). The GAM has been widely employed in the study of catch per unit of effort (CPUE) (Dai et al., 2011; Lu et al., 2013) and its correlation with environmental factors (Li et al., 2015b; Solanki et al., 2017). In the present study, monthly variations in PP and their relationship with environmental factors in the Daya Bay were systematically analyzed by using GAM combined with field data to provide scientific evidence facilitating the sustainable development of marine fisheries and basic information for monitoring and prevention of algal blooms in the Daya Bay.
2 Materials and methods
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2.1 Research dates and area
A total of 15 stations were set up in the Daya Bay (Fig. 1) for use in a survey that was conducted from January to December 2016. During the study period, there was one cruise survey in the middle of each month and a total of 12 cruises.
2.2 Sampling and analytical methods
PP in five stations were measured by the 14C tracer method (Parsons et al., 1984). Water samples were taken from six different depths. Samplings at stations with euphotic zone were at the surface and the 50%, 30%, 10%, 5% and 1% daylight level. Acid-cleaned polycarbonate bottles (two in light and one in dark for each depth) were filled with seawater filtered through 200 μm mesh. Samples were incubated in situ for 2–6 h after adding 105 kBq of NaH14CO3 tracer, and then filtered onto 0.22 μm polycarbonate filters (Millipore) respectively at 0.04 MPa vacuum pressure immediately. Filters were soaked in 1 mL 0.1 μmol/mL HCl and allowed to stand in uncapped polycarbonate 20 mL vials 15 min and stored in dark. Ten microliter cocktail (Perkin Elmer) was added after samples taken to laboratory. Total radioactivity counting on filters was performed on a Tri-Card 3110TR Liquid Scintillation Counter. PP in the other ten stations were calculated by the assimilation index method (Cadée, 1975).
Chlorophyll (Chl a) were sampled from the same primary production sampling layer and measured by filtering 370 mL of seawater through Whatman (φ25 mm) GF/F filters. The filters were kept frozen in the dark for 24 h before they were extracted in 90% acetone. The Chl a concentrations were then measured by the fluorometric method according to the formula proposed by the Specifications for Oceanographic Survey (Yentsch and Menzel, 1963) using a Turner Designs 10-AU-005-CE Fluorometer.
Physical parameters (temperature and salinity) were recorded using the HQ Series Portable Meters (HQ40d). PARs were collected data products in MODISA map released by NASA (time accuracy of daily, spatial resolution of 4 km).
The measure of transparency was calculated using the secchi disc (Tyler, 1968). For phytoplankton quantitative analysis, the settlement method described by Sukhanova (1978) was adopted. Numerical analysis was conducted using an inverted plankton microscope. Phytoplankton was identified according to the identification guides described by Tomas (1997).
2.3 Data processes
The GAM can be used to deal directly with nonlinear relationships between response variables and multiple explanatory variables. The model is generally expressed as follows:
$g\left({{u_i}} \right) = {\beta _0} + \mathop \displaystyle\sum \limits_{{{i}} = 1} {f_i}\left({{x_i}} \right) + {\varepsilon _i}, $
where g is a known monotonic link function and ${u_i} = E\left({{Y_i}} \right)$ is known explanatory variable of the ith response variable, ${\beta _0}$ is the parameter of model estimation, ${Y_i}$ is the ith response variable, ${f_i}$ is smooth, but unknown functions of any number of covariates, and ${\varepsilon _i}$ is the stochastic disturbance term. Based on the significant relationships between different explanatory variables and the response variable (PP), we selected correlated variables for the GAM in this study. The initial GAM is expressed as follows:
ln(PP)=s(longitude)+s(depth)+s (surface Chl a)+s(bottom Chl a)+s(surface temperature)+s(surface salinity)+s(bottom temperature)+s(bottom salinity)+s(surface phytoplankton abundance)+s(bottom phytoplankton abundance)+s(PAR)+s(transparency)+s(month)+${\varepsilon _i}$,
where s are smoothing splines, the survey month is a categorical discrete variable and the remaining parameters are continuous variables. Statistical analyses were performed using the R package (mgcv) (Wood, 2011) and Microsoft Excel.
3 Results
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3.1 Primary production
3.1.1 Horizontal distribution
The PP in the Daya Bay reached relatively high levels in the coast of the world wide, with wide variations between stations over the year; however, there was no clear trend in the horizontal distribution for each month (Fig. 2). High PP values appeared at Dapengao in January and February. The high values gradually shifted toward Dalajia in March and receded to the area between the inlet and outlet of the Daya Nuclear Power Plant in April. The high values further extended toward the harbor in May and June, with the highest being observed at the port in May. Relatively low PP values were always distributed within Aotou Port from January to June, while high values were observed from July to November. The range of variations in PP gradually decreased in December until there was an even distribution in the bay.
3.1.2 Vertical distribution
The vertical distribution of PP in the Daya Bay is shown in Fig. 3. Since the euphotic layer varied for each station, we used light depth as the ordinate to display the vertical distribution of PP for each month. Photosynthesis can be suppressed by overly-strong light and ultraviolet light; thus, the most vigorous photosynthesis in some sea areas is often not found on the surface of the ocean, whereas photosynthesis rapidly increases with increasing depth in the subsurface until reaching a maximum, after which it decreases exponentially with depth. The PP showed a monomodal vertical distribution for each month. In the present study, the maximum PP occurred at a light depth (0.5–4 m) of 50% surface illumination in the Daya Bay, while the minimal PP was observed at a light depth of 1% surface illumination.
3.1.3 Productivity index
The productivity index represents the mass of organic carbon assimilated by per unit mass of Chl a per unit of time (Lalli and Parsons, 1997). This index is an indicator of the physiological state of phytoplankton and a good indicator for assessing the quality of marine environments. The results show that the annual average productivity index was 2.42 h–1 in the Daya Bay, and the spring, summer, autumn, and winter productivity index values were 1.86, 2.47, 3.10, and 2.24 h–1, respectively. From the monthly perspective, the productivity index was highest in January and lowest in May. In terms of monthly variations (Fig. 4), the productivity index reached the highest value at a light depth of 50% surface illumination, which was followed by the light depth of 100% surface illumination. Conversely, the lowest productivity index appeared at a light depth of 1% surface illumination. At light depths of 100%, 50%, and 30% surface illumination, peak productivity occurred in June, while valleys appeared in March and August. However, at light depths of 10%, 5%, and 1% surface illumination, peak productivity occurred in January, while valleys appeared in May. The above results indicate that the productivity index had no large variations in this sea area, while the measured values varied between 1.09 and 4.81 h̵1 at various stations. These values were also close to the average productivity index of phytoplankton in the Daya Bay in earlier years, suggesting that the phytoplankton were in a normal physiological and biological state in this area.
3.1.4 Monthly variations
The monthly average PP in the Daya Bay ranged from 48.03 to 390.56 mg/(m2·h), with an annual average of 182.77 mg/(m2·h). The monthly variation patterns of PP are shown in Fig. 5. The PP exhibited significantly different monthly variation patterns in the Daya Bay from January to December in 2016. The dynamic variation curve of PP showed a relatively low value of only 133.18 mg/(m2·h) in January. Starting from this point, the overall PP continued to increase from January to March, until it decreased slightly in April. Shortly thereafter, the value increased sharply in May and peaked at 390.56 mg/(m2·h) for the whole year. There was a continuous decline from June to November, when the lowest value of 48.03 mg/(m2·h) occurred. The value once again increased sharply in December.
3.1.5 Seasonal variations
The seasonal averages of PP were 273.94, 224.12, 92.54, and 140.47 mg/(m2·h) for spring (March–May), summer (June–August), autumn (September–November), and winter (December–February), respectively (Fig. 6). With regard to seasonal variations, the overall PP ranked spring>summer>winter>autumn, and the spring level was approximately three times the autumn level. The seasonal variations in PP were found to be generally consistent with those of the Chl a concentration. The Chl a concentration reflected the phytoplankton standing stock in water and to some extent determined the level of PP. Therefore, these results indicate that the PP occurred at a correspondingly high level in the Daya Bay for the month with high phytoplankton standing stock.
3.2 GAM analysis
3.2.1 Statistical distribution of the response variable ln(PP)
The results of a Kolmogorov–Smirnov test show that ln(PP) tended to follow a normal distribution (μ=4.84, σ=0.93). The sample quantiles and theoretical quantiles of ln(PP) were almost normally distributed on a straight line (Fig. 7). The response basis of a normal distribution is a precondition for further explanation by GAM. Therefore, it is reasonable to describe the response variable using GAM in this study.
We performed an F-test on different variables of the initial GAM. Temperature, bottom salinity, phytoplankton abundance, and PAR had no significant relationship with the response variable PP (p>0.05). Longitude, depth, and surface salinity were significantly related to PP (p<0.05) (Table 1). Chl a and transparency were significantly related to PP (p<0.01). By sequentially introducing significant variables into the GAM, the model can be expressed as
ln(PP)=s(longitude)+s(depth)+s(surface Chl a)+s(bottom Chl a)+s(surface salinity)+s(transparency)+s(month)+${\varepsilon _i}$.
The PP decreased gently as longitude increased (Fig. 8a). For surface Chl a, the PP increased rapidly with increasing surface Chl a concentration at 0–20 mg/m3, then increased in a wave pattern with increasing surface Chl a concentration at 20–60 mg/m3. However, PP started to decline when the Chl a concentration exceeded 60 mg/m3 (Fig. 8c). In contrast, PP always increased slowly with increasing bottom Chl a concentration in the range of 0–55 mg/m3 (Fig. 8d). In addition, PP increased slowly as surface salinity increased, and the highest level was reached when the salinity was 31. Finally, PP declined slowly when the salinity was between 31 and 34 (Fig. 8g).
PP increased with increasing water depth to relatively high levels at a depth of 12 m at most stations, below which it remained relatively stable (Fig. 8b). In addition, PP increased with increasing transparency to 7 m, but began to decline when the transparency exceeded this depth (Fig. 8l).
4 Discussion
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The PP in the Daya Bay is influenced by physicochemical and biological factors. The most important physicochemical factors are light intensity and nutrients as well as hydrological conditions associated with these two factors, which directly relate to the physiological process of phytoplankton photosynthesis. The biological factors are mainly phytoplankton biomass and zooplankton feeding, which constantly change under natural conditions (Shen et al., 2010).
Variations in these biotic and abiotic factors are strongly influenced by the hydrological environment. The line chart unfolds a comparison of flow direction in the Daya Bay between summer and winter (Fig. 9). In summer, the current direction points to the inside of the Daya Bay both in the middle and bottom layers, while point to outside of the bay in the upper layer. It was demonstrated that there were completely opposite circulation structures in the upper and lower layers of the Daya Bay. The southwest wind prevailed in the South China Sea during the summer. Under its effect, an upwelling occurs on the coast of eastern Guangdong Province, and brings high-salt cold water from the subsurface of the open sea into the lower inner layer of the bay to replace the water from the upper of the bay. In winter, the upper, middle and lower seawaters in the bay all flow clockwise. Contrary to the summer, the northeast monsoon prevails in the coast of eastern Guangdong Province. Affected by this, the seawater moves toward the southwest and is blocked by the Dapeng Peninsula and Sanmen Island in the west of the bay, forming a clockwise pressure gradient, may be the main driving force for driving an anticyclonic horizontal circulation.
Obviously, in summer, seawater shows an upwelling structure, while in winter it presents a circulation structure. These two different phenomena cause a series of changes in the physical, chemical, and biological processes. Many environmental factors are affected, resulting in large differences in biomass and productivity of phytoplankton in the Daya Bay.
4.1 Influence of space effect on PP
GAM analysis showed that PP decreased slowly as longitude increased. However, as shown in Fig. 2, the distribution of PP gradually increased from coastal to offshore areas in May, June, and December. These findings indicate that PP was not only related to geographical location, but also influenced by other factors such as depth.
The Daya Bay is surrounded by mountains on three sides and greatly influenced by terrestrial climate. The depth of the Daya Bay ranges from 5 to 26 m, with an average of 12 m. The bay is characterized by high transparency in relatively deep waters in the mouth and center of the bay, low transparency in the coastal waters and the inner waters of the bay. The tides in the Daya Bay are informal semi-diurnal tides that have weaker water exchange capacity in summer than winter (Wu et al., 2007). Overall, the Daya Bay has relatively high water exchange ability, but this differs horizontally. Relatively speaking, water is shallow, the sea floor is flat, and the flow field is regular in the Daya Bay. As distance from the coast increases, water depth becomes deeper and less affected by the terrestrial environment. Our results show that the PP reached higher levels at a depth of 12 m. In the coastal area affected by terrestrial pollution, suspended particles reduced water transparency and the photosynthetic capacity was limited, which decreased the PP. As the distance from the coast increased, the concentration of suspended particles declined and the photosynthetic capacity improved, resulting in increased of PP.
4.2 Influence of time on PP
The present study was conducted from January to December in 2016. The highest PP over the study period was 390.56 mg/(m2·h) in May, while the lowest was 48.03 mg/(m2·h) in November. The difference between these two values was more than seven times.
On a long-term scale, seasonal changes had a significant influence on PP. During spring, water temperature generally increased because of increasing temperature and the rich nutrient salts stored in the winter provided favorable conditions for the reproduction and growth of spring phytoplankton. As a result, the PP increased substantially, reaching the highest levels in May. During summer, phytoplankton remained at relatively high levels because of the high water temperatures and good light conditions. In autumn, a large amount of nutrients was consumed after the massive production of phytoplankton, so the PP markedly decreased and remained at low levels across the bay, with the lowest levels occurring in November. During winter, the PP was greatly increased despite the low water temperature because of a bloom of Phaeocystis globosa, which is resistant to low temperature.
4.3 Influence of biomass on PP
The main foundation for PP is the process of photosynthesis, in which phytoplankton transforms light energy into chemical energy through Chl a. Therefore, regression analysis of PP was conducted and Chl a was measured at the stations, which yielded the following equation: PP = 0.181 6×Chl a (r=0.728, n=60, p<0.001) (Fig. 10). There was a significant correlation between Chl a and PP in the Daya Bay at the 95% confidence intervals; therefore, this equation provides a reliable empirical formula for estimating the PP in the study area using the productivity index method (Lalli and Parsons, 1997).
4.4 Influence of phytoplankton on PP
GAM analysis showed that there was no significant correlation between PP and phytoplankton. However, the results revealed that variations in dominant phytoplankton species were related to PP to a certain degree. The major dominant species of phytoplankton showed significant seasonal succession in the Daya Bay and varied from month to month. As shown in Fig. 5 and Fig. 11, the PP and phytoplankton abundance followed completely opposite trends in February–April and June–September, whereas they showed consistent trends in other months. This may have been because of inconsistent individual sizes of phytoplankton cells. The Daya Bay underwent two unique hydrological processes; namely, the Zhujiang Estuary coastal current in February–April and wind-driven upwelling in June–September. Both of these processes led to introduction of algae with relatively large individual cell volumes, but a small cell number. For example, the dominant phytoplankton species in August were Scrippsiella trochoidea and Skeletonema costatum, which were larger than those species (Chaetoceros constrictus and Pseudo-nitzschia delicatissima) presenting in September, while the dominant phytoplankton species in July were Asterionella japonica, which were even larger than species (Scrippsiella trochoidea and Skeletonema costatum) observed in August. These contrasting trends in phytoplankton number and cell volume led to the completely opposite trends of PP and phytoplankton abundance.
4.5 Influence of other environmental factors on PP
4.5.1 Temperature and salinity
(1) Seasonal variation
The Daya Bay is located in a subtropical sea area. In 2016, water temperatures were relatively high (14.8–33.4°C) and suitable for phytoplankton reproduction and growth. In the present study, GAM analysis revealed that water temperature did not significantly influence PP. Temperature and salinity can reflect the hydrological features of the ocean. In April (rainy season), seawater temperature increased and surface seawater salinity dropped to 30.91. Surface flow caused by rainfall brought rich nutrients to the sea and allowed massive reproduction of phytoplankton; therefore, the PP showed a gradual upward trend. In summer, the southwest monsoon prevailed, which drove upwelling in coastal areas of eastern Guangdong Province. The extension of the upwelling in eastern Guangdong Province obviously controlled the hydrological conditions in the Daya Bay during summer and formed remarkable thermoclines and haloclines (Li, 1998), which directly influenced the ecological environment. The upwelling contributed to dominance of the eurythermal coastal species Asterionella japonica and the offshore eurythermal species Scrippsiella trochoidea, which were characterized by a small quantity and a large volume. Because of this change, bottom Chl a gradually decreased; therefore, PP was eventually reduced in the strengthening process by upwelling.
During autumn, the vertical stratification disappeared in mid-October, and the distribution of isohalines gradually shifted toward the winter type by mid-November. When surface offshore waters from the South China Sea invaded the coastal areas of eastern Guangdong Province, numerous oceanic warm water species were carried. Consequently, the total phytoplankton abundance generally declined when compared with that in September, and the PP gradually decreased.
During winter, the northeast monsoon began to prevail in December, which drove the occurrence of southwestward coastal currents in the coastal area of eastern Guangdong Province (Yin et al., 2006). Driven by the low-temperature and low-salinity coastal waters of eastern Guangdong Province, the eurythermal coastal species Phaeocystis globosa (prymnesiophyta) bloomed in offshore waters of the Daya Bay mouth and became the major dominant species during December, which to some extent improved the PP compared with that in autumn.
(2) Thermal discharge
The nuclear power plant cooling waters has a strong thermal shock effect on the surrounding marine organisms, especially for phytoplankton, the primary producers who play a key role in the marine ecosystem (Barnett, 1972).
The long-term changes in the phytoplankton community structure under the influence of nuclear power plant thermal effluent in the Daya Bay have been studied in the summer season from 1982 to 2005. The results showed that water temperature at the outfall station was increased by 6.8°C during the 23-year study period. Dinoflagellates increased to about 50% of the total phytoplankton abundance. On the contrary, the diatoms contribution decreased from 82% in 1982 to 53 % in 2005 (Li et al., 2011). These results suggest that phytoplankton community has a clear trend of transformation from diatoms to dinoflagellates when temperature increases to a threshold level in the Daya Bay. The survey of relevant scholars in recent years has also confirmed this view (Tang et al., 2013). Yu et al. (2007) comprehensively analyzed the historical observation data of the Daya Bay from 1970 to 2005 and satellite remote sensing images from 1997 to 2004. The results indicated that the annual mean surface water temperature and Chl a contents increased by 1.1°C and 1.9 mg/m3, respectively after 1994, due to the influence of thermal discharge from the nuclear power plant.
In the current study, the phytoplankton biomass represented by Chl a concentration is also generally accepted as an indicator of water quality, algal bloom, and productivity (Boyer et al., 2009). There is a significant positive correlation between Chl a and PP of phytoplankton in the Daya Bay (Fig. 10). There are reasons to believe that thermal discharge can increase the concentration of Chl a within a certain extent, while it will promote PP at the same time.
4.5.2 Light
(1) Photosynthesis
As shown in Fig. 12, measurements of photosynthesis rate show that maximum production occurs in the subsurface, usually somewhere between 0.3 m and 4 m depending upon light intensity. The most vigorous photosynthesis is often not carried out at the surface in some natural sea areas. Photosynthesis is inhibited by strong light and ultraviolet end of the spectrum, which is harmful and depresses photosynthesis (Tait and Dipper, 1998).
The rate of photosynthesis increases with the enhancement of light intensity until light-saturated. In bright daylight the illumination usually light-saturated at the sea surface, whereas light-limited at the sea bottom. Therefore, although the sea bottom is rich in nutrients, photosynthesis is also very low due to light restrictions in the Daya Bay. When the bottom water diffuses through the critical depth to the subsurface layer within the euphotic zone, the maximum production occurs under sufficient light and nutrient having the most favorable effects.
(2) Transparency
Transparency is a measure of the visibility of seawater that reflects the turbidity. The light energy that penetrates into seawater is a requirement for photosynthesis by phytoplankton. The depth of seawater to which sunlight can reach determines the level of the productivity layer. Therefore, the transparency of seawater is an important influencing factor for PP. The results of regression analysis showed that there was a significant positive correlation between PP and transparency.
The analysis showed that offshore water could affect transparency in the Daya Bay, and thus influence the PP. The variations of transparency in the Daya Bay were related to different water masses in different seasons and the optical properties of offshore waters outside the bay (Fig. 13). During summer, the Daya Bay was subjected to cold water intrusion because of the effects of the southwestern monsoon, and a large amount of sediment was brought into the upper layer by strong upwelling, leading to an overall decrease in the transparency of seawater (1.87 and 2.23 m in June and July, respectively). In winter, the bay was dominated by coastal waters of eastern Guangdong Province, which had higher sediment content than the waters of the South China Sea; therefore, the transparency was generally reduced. This reduction was particularly high in December, when the average transparency of the sampling stations was only 2.29 m and the value was even lower in the bay mouth area, indicating a serious influence of coastal waters. Because transparency was significantly influenced in both of the critical oceanic hydrological processes during summer and winter, these findings further indicate that PP is also controlled by offshore water systems.
4.5.3 Nutrients
Economic development and population growth coupled with mariculture pollution caused tremendous changes in the trophic structure of sea areas of the Daya Bay in the early 21st century (Wang et al., 2008). Since 2000, the dissolved inorganic nitrogen (DIN) concentration has markedly increased, while the dissolved inorganic phosphorus (P) concentration has dropped, resulting in a sharp rise in the DIN/P ratio from 4.96 in 2000 to 31.49 in 2006 (Fig. 14). Meanwhile, the growth of phytoplankton shifted slowly from DIN limitation to P limitation (Wang et al., 2004a). From 2008 to 2011, the pollution situation has clearly improved. The concentrations of DIN and P have generally shown a downward trend, and the ratio of DIN/P has remained at a relatively low figure (<9). The DIN limitation indicated that the pollution level in the Daya Bay has declined. After 2011, the concentration of DIN and P showed an overall upward trend again, and the ratio of DIN/P also slowly increased from 7.52 in 2011 to 15.6 in 2016 (Fig. 14). Because of continuous pollution by large amounts of DIN, phytoplankton showed a trend of miniaturization, and the original dominant species with large sizes were gradually replaced by smaller ones. The frequency and number of non-diatom species such as dinoflagellates, prymnesiophyta, and cyanobacteria increased significantly (Wang et al., 2009). In the present study, although the dominant phytoplankton species mainly comprised small diatoms in most months, the diversity, number, and percentage of dinoflagellates were significantly higher than in previous surveys (Wang et al., 2014). Especially in January–April, dinoflagellates accounted for up to 72.5% of the total abundance on average, and this percentage fluctuated monthly (50.9% in January, 73.8% in February, 66.8% in March, and 98.4% in April). The PP also became significantly higher than before, indirectly suggesting the presence of nutrient pollution in the Daya Bay over recent years; accordingly, further study investigating pollution of the Daya Bay and its effects are warranted.
5 Conclusions
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The PP in the Daya Bay showed significant monthly variations, with the highest levels being observed in May and the lowest in November. The overall PP ranking were spring>summer>winter>autumn.
GAM analysis showed that the temperature, bottom salinity, phytoplankton, and PAR had no significant relationships with PP, while longitude, depth, surface salinity, Chl a and transparency were significantly related to PP.
Our analysis showed that the environmental factors significantly related to PP were controlled by changes in the monsoonal period and different terrestrial and offshore water systems. For example, the upwelling due to South China Sea water intrusion driven by the southwest monsoon led to decreased temperature, increased salinity, and an abrupt decrease in transparency throughout the sea area. Meanwhile, the upwelling brought the eurythermal coastal species Asterionella japonica and the offshore eurythermal species Scrippsiella trochoidea, which are characterized by small numbers and large volume. Consequently, the distribution of biomass and PP showed a gradually decreasing trend, despite being present at relatively high levels. In winter, the northeast monsoon prevailed in the Daya Bay, resulting in the formation of southwestwardly low-temperature low-salinity coastal currents in the coastal area of eastern Guangdong. The waters were well mixed and had low transparency. Therefore, the growth of phytoplankton was inhibited, and the dominant phytoplankton species changed from diatoms to dinoflagellates. In January, with phytoplankton abundance dropping to the lowest point of the year and phytoplankton biomass decreasing, the corresponding concentrations of PP in January remained stable lower than those in February to September.
Acknowledgements
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The authors thank colleague Liao Jianji and Zhang Ming for their assistance in sampling, and Ye Youyin from the Third Institute of Oceanography, Ministry of Natural Resources for phytoplankton data analyze.
Funding
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  • The National Natural Science Foundation of China under contract No. 41506136; the Scientific Research Foundation of Third Institute of Oceanography, SOA under contract No. 2015005.
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Appendix
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Year 2018 volume 37 Issue 12
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Article Info
doi: 10.1007/s13131-018-1281-6
  • Receive Date：2018-01-02
  • Online Date：2026-04-14
  • Published：2018-12-25
Article Data
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History
  • Received：2018-01-02
  • Accepted：2018-05-04
Funding
The National Natural Science Foundation of China under contract No. 41506136; the Scientific Research Foundation of Third Institute of Oceanography, SOA under contract No. 2015005.
Affiliations
    1 Laboratory of Marine Biology and Ecology, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China

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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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