Characteristics of Phytoplankton Productivity in Three Typical Lake Zones of Taihu, China

The statistical results of the environmental parameters for different seasons in the Taihu algae-type, transition, and grass-type zones are shown in Table 2. Most of these environmental factors significantly changed in different seasons/regions. The water temperature (T) shows the same pattern in the three lake zones, maintaining around 14 degrees in the winter, rising to over 30 degrees in the summer, and being slightly warmer in the fall than in the spring, in descending order as follows: summer > fall > spring > winter. Chlorophyll-a (Chl-a) and algal density (AD) are closely related to both season and temperature, as summer is the period of algal bloom with peak Chl-a and AD, while winter is the period of low temperatures when both Chl-a and AD reach their lowest levels. Chl-a is significantly higher in the fall than in the spring (p 29]. Chl-a and AD levels in the three zones differ considerably in the following descending order: algae-type zone > transition zone > grass-type zone.

Shallow water environments are more suitable for the growth of macrophytes due to photosynthetic requirements; meanwhile, phytoplankton are more competitive in deep water environments. The water depth (D) of the three types of zones conforms to this pattern, with the descending order being as follows: algae-type zone > transition zone > grass-type zone. D is significantly higher in the wet seasons (summer and fall) than in the dry seasons (winter and spring) (p 25]. The total suspended solid (TSS) levels are, therefore, higher in the winter and spring when the water level is shallower. Except for the algae-type zone, the TSS levels are higher in the summer and fall due to the extensive reproduction of phytoplankton.

Algae photosynthesis in summer and fall consumes carbon dioxide in the water column, which raises the pH level. The dissolved oxygen (DO) is lower in the summer and fall than in the winter and spring because the process of algal decomposition consumes a large amount of DO during the rainy seasons. In addition, the atmospheric reoxygenation process is inhibited by water blooms. DO is abnormally low in the grass-type region in the fall, at only 6.20 mg/L, and the reason for this is the consumption of oxygen through the decay and decomposition of macrophytes [24]. In the summer and fall, algal mortality and decomposition release large amounts of phosphorus into the water column, resulting in increased total phosphorus (TP) levels in the lake [30]. Regarding total nitrogen (TN), high temperatures in the summer promote denitrification, leading to the lowest nitrogen levels (p 23].

The seasonal variations in gross primary productivity (GPP_c), net primary productivity (NPP_c), and respiration (R_c) in the water column in the three zones are presented in Figure 2. The horizontal distribution of GPP_c, NPP_c, and R_c and the seasonal proportion of GPP_c in different regions are shown in Figure 3. It can be seen that GPP_c in Meiliang Bay (algae-type zone) varies between 1.50 ± 0.12 and 7.20 ± 0.69 g O₂ m⁻² d⁻¹, with a mean of 4.41 ± 0.46 g O₂ m⁻² d⁻¹; in Gonghu Bay (transition zone), it varies between 0.92 ± 0.05 and 5.86 ± 0.68 g O₂ m⁻² d⁻¹, with a mean of 3.32 ± 0.29 g O₂ m⁻² d⁻¹; and in East Taihu Bay (grass-type zone), it varies between 0.78 ± 0.10 and 2.42 ± 0.47 g O₂ m⁻² d⁻¹, with a mean of 1.45 ± 0.16 g O₂ m⁻² d⁻¹. The highest GPP_c values in the three lake zones always appear in the summer, while the lowest values appear in the winter, and GPP_c is slightly higher in the fall than in the spring, in descending order as follows: summer > fall > spring > winter.

R_c and NPP_c are largely consistent with GPP_c in their temporal trends. In the algae-type zone (Figure 2a), phytoplankton production (GPP_c) is higher than respiration (R_c) in the spring, summer, and fall, resulting in positive NPP_c values in these seasons. NPP_c is negative in the winter due to the weaker phytoplankton photosynthesis at lower water temperatures. The transition zone shows the same trend as the algae-type zone (Figure 2b). However, in the grass-type zone (Figure 2c), where macrophytes are dominant, phytoplankton production (GPP_c) is only higher than respiration (R_c) during the summer period.

Water column primary productivity (GPP_c) and respiration (R_c) are both highest in Meiliang Bay and lowest in East Taihu Bay (Figure 3a), with a decreasing order as follows: algae-type zone > transition zone > grass-type zone. Overall, GPP_c is greater than R_c except in the grass-type zone, where they are essentially equal. NPP_c is at a low level as a whole. From Figure 3b, we can observe that the seasonal proportion of GPP_c in Lake Taihu varies markedly, but the seasonal proportion does not differ much among different subregions, with approximately 40% in the summer, 25% in the fall, 20% in the spring, and less than 15% in the winter.

The vertical distribution of P_g, P_n, and R in the different zones is shown in Figure 4. It is clear that primary productivity is highest in the algae-type zone (Meiliang Bay) and lowest in the grass-type zone (East Taihu Bay) at different vertical depths (Figure 4a). The highest values of P_g and P_n in the three zones were found at a depth of 0.2 m in Meiliang Bay, being 8.55 mg O₂ L⁻¹ d⁻¹ and 2.56 mg O₂ L⁻¹ d⁻¹, respectively. P_g and R are relatively low and stable in the different layers of East Taihu Bay because this area is generally dominated by macrophytes with less phytoplankton present. Light intensity decreases with water depth, as does P_g (Figure 4a), which is consistent with findings for Dianshan Lake [31]. Most of the highest values of primary productivity were at depths of 0.2 m due to photoinhibition in the surface (0 m) layer [32]. In general, the percentages of P_g at water depths of 0 m, 0.2 m, 0.4 m, 0.6 m, 0.8 m, and 1 m were 23%, 31%, 23%, 11%, 7%, and 5%, respectively. Primary productivity and respiration begin to decrease rapidly when the water depth reaches 0.4 m (Figure 4a,c) because light intensity decreases rapidly at this depth. Combined with the transparency (SD) data in Table 2, the optical compensation depth, i.e., the depth of the water layer with P_n = 0 (Figure 4b), is derived as approximately 0.8 times the transparency for Lake Taihu.

The vertical distribution of P_g, P_n, and R in different seasons is shown in Figure 5. It is obvious from Figure 5a,c that P_g and R in different water layers exhibit a pattern of summer > fall > spring > winter. Surface productivity remains the main contributor to water column productivity in different seasons, and the highest values of P_g in the spring, summer, and fall still appear at a depth of 0.2 m and decrease with water depth, as previously described. In the winter, the temperature is low, and the light intensity is weak, and P_g maintains a small value in each layer and does not change significantly, which is consistent with the findings in some ponds [33]. The respiration (R) did not vary much between water levels, especially in the dry seasons (winter and spring), when the productivity was greater than respiration (i.e., P_n > 0) only at the surface of the water column (0 m and 0.2 m). In the fall, P_n remains positive until the water depth is 0.4 m, while in the summer, it remains positive until a depth of 0.6 m (Figure 5b). This means that, during periods of higher temperatures, there are more layers of water where production is higher than respiration in Lake Taihu. When the water depth reaches a certain level (D > 0.8 m), P_n stops changing in all seasons and stays around −0.3 mg O₂ L⁻¹ d⁻¹.

Spearman correlation analysis was used to determine the correlations between GPP_c and environmental factors in Meiliang Bay, Gonghu Bay, and East Taihu Bay, and the results are shown in Table 3, Table 4 and Table 5. It can be seen that GPP_c had a significant positive correlation with T, Chl-a, and D in Meiliang Bay. In addition, it was also positively correlated with TSSs (Table 3). In Gonghu Bay, GPP_c was positively correlated with Chl-a, T, and D (p Table 4), and in East Taihu Bay, GPP_c was positively correlated with T (p Table 5).

It is clear from the above that phytoplankton productivity is mainly related to T, D, Chl-a, and TSSs. In particular, temperature (T) and water depth (D) showed a positive correlation with GPP_c in all three lake zones. In our previous study [23], it was found that nutrients (TP and TN) are also important influencing factors of GPP_c, but here, there is no obvious correlation between nutrients and GPP_c. The reason for this is that previous studies have focused on specific periods or seasons, with nutrients having their influence, while the present research included a four-season study at a particular time; thus, those factors that vary significantly with the seasons, such as T and D, had a more obvious impact on GPP_c. In addition, Chl-a showed a highly significant correlation with GPP_c in Meiliang Bay and Gonghu Bay, while they were not significantly correlated in East Taihu Bay. This is due to the fact that there is only a small amount of phytoplankton in the grass-type zone (East Taihu Bay), resulting in insignificant changes in GPP_c with Chl-a [34]. The correlation coefficients of GPP_c and Chl-a for the three zones are in decreasing order as follows: Meiliang Bay (0.933) > Gonghu Bay (0.881) > East Taihu Bay (0.636). This implies that the more the zone is dominated by planktonic algae, the greater the correlation between GPP_c and Chl-a.

It is worth noting that GPP_c and TSSs showed a positive correlation (with a correlation coefficient of 0.708 (p Table 3), while in the grass-type zone, they had a significant negative correlation (with a correlation coefficient of −0.954 (p Table 5). GPP_c in the algae-type zone mainly comes from productivity during the wet seasons (summer and fall), when the massive growth and then death of algae lead to an increase in TSSs; thus, GPP_c has a positive correlation with TSSs. However, in the grass-type zone with less planktonic algae and a shallower water depth, the increase in TSSs is mainly due to the suspension of bottom sediments caused by wind and wave disturbances [25,35], which reduces the water transparency and the depth of the euphotic zone, thus inhibiting the photosynthesis of algae [36].

Multiple stepwise regression analysis was used to test the main influencing factors on GPP_c, and the results are shown in Table 6. T, D, Chl-a, and SD can be used to estimate GPP_c in different regions. The regression equation for GPP_c in Meiliang Bay is GPP_c = 0.839 T + 0.380 SD + 0.329 D—T alone accounts for 80.9% of phytoplankton productivity, and T, SD, and D jointly account for 95.5% of GPP_c. The equation in Gonghu Bay is GPP_c = 0.916 Chl-a + 0.175 SD, and Chl-a alone accounts for 95.4% of GPP_c. In East Taihu Bay, the equation is GPP_c = 0.647 T + 0.416 D, accounting for 93.0% of GPP_c (R² = 0.930). In the equation for Meiliang Bay, T and D can be represented by Chl-a because they have a highly significant correlation (p Table 3). Similarly, in the equation for Gonghu Bay, SD can be represented by T (0.831, p Table 4), and in East Taihu Bay, D can be represented by TSSs (0.892, p Table 5). Summarizing the above analyses, the main influencing factors on phytoplankton productivity are T, D, Chl-a, and SD in Meiliang Bay and Gonghu Bay and T, D, and TSSs in East Taihu Bay.