The Impact of Artificial Water Diversion–induced Algal Blooms on Carbon Balance in a Semi-enclosed Bay

Abstract

Artificial water diversion is widely used to address water security; yet, its impacts on phytoplankton communities and coastal carbon balance remain poorly understood. Using a seasonal diversion project in a semi-enclosed bay as a case study, we analyzed phytoplankton composition via morphological methods and assessed carbon balance through simultaneous measurements of primary production (P), ecosystem respiration rate (R), and production-to-respiration (PP/R) ratio. Our results showed that artificial water diversion activities during the wet month enhanced hydrological connectivity and phytoplankton homogeneity, triggering a mixed diatom–dinoflagellate bloom. Phytoplankton abundance during the wet month increased by sevenfold (surface layer) and 26.5-fold (bottom layer) compared to dry month values. This simultaneously resulted in the PP value of the wet month being more than twice that of the dry month. Although R rose with increasing phytoplankton abundance, no significant correlation was observed between them. Instead, dry-month R was primarily driven by pH and dissolved organic carbon, whereas wet-month R showed minimal environmental linkages. PP/R ratios of surface and bottom layers were always less than 1, implying Meishan bay was a net heterotrophic ecosystem, despite significant changes in phytoplankton community structure induced by artificial water diversion and associated algal bloom. Furthermore, our results strongly suggest that changes in PP, but not in R, control the PP/R ratio of Meishan bay. This study offers valuable guidance for the ecological management of artificial water diversions and can serve as a reference for similar water diversion projects in other semi-enclosed bays.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00248-025-02588-z.

Introduction

Water diversion projects have been globally adopted as a key engineering strategy to mitigate water scarcity and improve environmental conditions for decades [1]. Although water diversions have significantly bolstered local social and economic progress, mounting apprehensions have surfaced regarding their detrimental ecological impacts [2, 3]. Water diversions may lead to the alteration of natural habitats, expansion of invasive species, and biotic homogenization, ultimately causing a decline in the functionality and services of the ecosystem [4, 5]. However, current monitoring efforts of water diversions primarily focus on physicochemical parameters such as hydrological regimes, organic contaminants, and eutrophication, revealing a critical deficiency in biological assessment [6, 7].

Phytoplankton communities are highly responsive to changes in the aquatic environment, making them valuable indicators for assessing the impact of water diversion [8, 9]. Water diversion–induced hydrological alterations modify key environmental factors including salinity, nutrients, and light availability, which finally affects the structure of the phytoplankton communities, even causing algal blooms [10–13]. Moreover, phytoplankton play vital roles in nutrient recycling and energy flow and are recognized as autotrophic components [14]. Primary productivity (PP) is the sum of all aquatic photosynthesis, mainly contributed by benthic and planktonic algae, while ecosystem respiration (R) includes the autotrophic and heterotrophic respiration of all aquatic organisms [15]. The production-to-respiration (PP/R) ratio partially characterizes the input/output ecosystem carbon balance. For a marine system, R > PP indicates a heterotrophic ecosystem [16], which respires more carbon than is produced by primary producers. When PP > R, the ecosystem can accumulate or export organic carbon. However, the current understanding of water diversion impacts on phytoplankton community and aquatic carbon balance remains limited [17]. More importantly, most previous coastal studies have examined the PP/R ratio only in the surface layer [18]. In aquatic ecosystems, phytoplankton fix organic carbon at the surface layer where light is available for photosynthesis, but consumption of organic carbon takes place not only through the water column but also in the sediments. Consequently, simultaneous quantification of PP/R ratios in both surface and bottom layers becomes imperative for accurately evaluating potential organic carbon subsidies from water diversions to coastal systems [19].

Meishan Bay is located on Meishan Island in Ningbo, Zhejiang Province, China. Two manually controlled dams were constructed at its northern and southernmost ends between 2012 and 2017 for artificial water diversion activities. The water in Meishan Bay primarily comes from the East China Sea outside the north dam and from more than ten land-based river channels inside the two dams. Then, the water exits the bay through the south dam. To ensure the relative stability of the water level throughout the year, the two dams are closed during the dry months (November to May) and opened for 4 h on rainy and typhoon days during the wet months (June to October). Moreover, the drainage gates of the land-based river channels inside the two dams are closed daily, except after rainfall and typhoons during the wet months. Under industrial and agricultural activities, the land-based river channels receive large amounts of chemicals, organic pollutants, and nutrients, which results in changes in environmental factors such as the accumulation of total phosphorus and nitrogen and water eutrophication [12, 13]. Our previous research indicated that the opening of two dams and river channels after a typhoon could rapidly and seriously affect the phytoplankton community and diversity in Meishan Bay, even causing harmful algal blooms [20].

In this study, taking the artificial water diversion activities in Meishan Bay as an example, we aimed to investigate (1) the ecological effects of water diversion activities on phytoplankton abundance and spatial distribution; (2) how the distribution of surface and bottom PP, R, and PP/R ratios developed under the water diversions; and (3) which environmental factors most affected the carbon balance of a semi-enclosed bay. We hypothesized that water diversion activities would not significantly alter the bay’s carbon balance, despite observable shifts in phytoplankton community structure caused by water diversions.

Materials and Methods

Sampling Sites and Collection

A total of 16 water samples were collected from the surface layer (0.5 m below the water surface) and bottom layer (0.5 m above the sediment) using a 10 L Nansen water sampler (Hydro-Bios, Germany), respectively. Sampling activities were conducted from January 5 to 7 (referred to as the dry month) and from September 4 to 6 (cloudy days after Typhoon Haikui; referred to as the wet month) in 2023 in the semi-enclosed Meishan Bay in Ningbo, China (Fig. 1). Typhoon Haikui influenced Meishan Bay from September 1 to 3, 2023. During this period, two dams and drainage gates of river channels opened 4 h each day, discharging substantial rainfall-derived freshwater into the bay. Stations 0, 1, and 2 were directly connected with the East China Sea and located outside the north dam (ON). Stations 3–15 were situated between the north dam and south dam. Among these, stations 3, 4, 6, 7, 9, 10, 12, and 14 were positioned at the mouth of the land-based river drainage gate (MG), whereas stations 5, 8, 11, 13, and 15 were located in the middle of Meishan bay (MB) (Fig. 1). Surface samples collected during the dry and wet months were designated as D0–D15 and W0–W15, respectively, while the corresponding bottom samples were labeled DB0–DB15 (dry month) and WB0–WB15 (wet month).

Fig. 1.

The locations of the sampling stations in Meishan Bay (ON: outside the north dam; MB: middle of Meishan Bay; MG: mouth of land-based river drainage gate; S: station)

To quantify phytoplankton abundances, 1 L of each water sample was fixed with 1% Lugol’s solution. Lugol’s iodine reagent-fixed water sample was concentrated at 30 mL after letting it stand for at least 24 h. Thereafter, the phytoplankton samples were homogenized via gentle shaking and then characterized and enumerated using a counting chamber and a light microscope (Leica DM500, Germany), with a minimum of 200 cells counted per sample. The water temperature, salinity, and pH were measured in situ using a multi-parameter probe (YSI EXO2, USA), while depth was obtained in situ using a portable digital depth sounder (Hondex PS-7, Japan). Phosphate, silicate, nitrate, nitrite, ammonium, chemical oxygen demand (COD), total carbon (TC), total organic carbon (TOC), and dissolved organic carbon (DOC) concentrations were measured using standard methods, which have been described by AQSIQ [21]. The total inorganic nitrogen (TIN) was defined as the sum of nitrite, nitrate, and ammonium nitrogen concentrations. For the measurement of chlorophyll a (Chl-a) concentration, 500 mL of water was gently filtered through a 0.22-μm cellulose filter and extracted in 90% acetone at 4 °C for 24 h in darkness. Chl-a concentrations were determined fluorometrically (Turner designs 10 AU fluorometer, USA) before and after acidification, following the methodology of Parsons et al. [22]. Moreover, Meishan bay is equipped with a long-term monitoring buoy station (29.756°N, 121.916°E) near station 15 [20], which records chlorophyll a concentrations hourly based on a probe (YSI 6600, USA). These data were used to identify algal bloom occurrences in Meishan Bay and determine the optimal sampling times.

Primary Productivity Estimation

Primary productivity was measured using the ¹³C assimilation method [23]. Acid-cleaned polycarbonate bottles (250 ml, Nalgene, USA) were filled with seawater that had been filtered through a 200-μm mesh filter, and then it was inoculated with a certain amount of NaH¹³CO₃ to ensure that the percentage of ¹³C in the inorganic carbon was between 6 and 11%. The samples were incubated on the deck using sunlight as the light source for 4–6 h, and the incubators were cooled with running surface seawater. The samples were filtered through GF/F 0.7 μm pore-sized filters (25 mm, Whatman, USA, calcined at 450 °C for 4 h in advance) under low vacuum (100 mmHg). The filter papers were treated with HCL gas for 2 h to remove any carbonate and were completely dried in a vacuum desiccator. The concentrations of the isotopic ratio of ¹³C were determined using an elemental analyzer and isotope ratio mass spectrometer (Thermo, USA). The formulas proposed by Hama et al. [23] were used to calculate the photosynthetic rate of the phytoplankton (mg C m^–3 h^–1), and the Chl-a concentration and light duration (12 h) were used to calculate the PP (mg C m^–3 d^–1).

Ecosystem Respiration Rate Estimation

Electron transport system (ETS) activity measurement has gained acceptance as a routine technique for estimating ecosystem respiration due to its high sensitivity [24]. The use of the ETS activity as an estimate of the respiration rate is based on the reduction of the tetrazolium salt 2-para (iodophenyl)−3(nitrophenyl)−5(phenyl) tetrazolium chloride (INT), which passively penetrates into the cell [25], by dehydrogenase enzymes present in the ETS, forming insoluble formazan crystals (INT-F).

We followed the protocol described by Hatzinger et al. [26] with some modifications. The seawater was passed through a 200-μm mesh filter to remove large algae and predators. The incubation devices were made of polypropylene, and all of the sample volumes were 280 mL. The measurements were conducted on six replicates. Three replicates were immediately fixed by adding formaldehyde (2% w/v final concentration) and were used as the killed controls. Fifteen minutes later, all of the replicates were inoculated with a sterile solution of 7.9 mM INT to a final concentration of 0.2 mM. After 6 h of incubation in the dark, three samples were fixed by adding formaldehyde in the same way as for the killed controls. After at least 15 min, six replicates were filtered through a 0.2-μm pore-size membrane (47 mm diameter polycarbonate, Millipore, USA), air-dried for ~ 1 min, and stored frozen in 2 mL cryovials at –20 °C. One milliliter of propanol was added to all of the cryovials, and the INT-F was extracted by initially sonicating the cryovials for 20–30 min in 50 °C water in an ultrasonic bath (Branson, USA) at a frequency of 47 kHz, followed by shaking for 2 min. One milliliter of propanol extract containing the INT-F was transferred to 2-mL microfuge vials and then centrifuged (Thermo, USA) at 13,200 g for 10 min at 18 °C. The absorbance at 485 nm was then measured using a spectrophotometer (Bio-Rad, USA). The INT-F concentration was calculated by applying a standard curve previously elaborated upon using seven different concentrations (ranging from 0.05 to 60 µM) of pure INT-F (Sigma, USA) dissolved in propanol. We used an R/ETS ratio of 12.8 [24], which meant a value of 1 µM INT-F was equivalent to 12.8 µM O₂. The O₂ consumption was converted to carbon units using a respiratory quotient (RQ) of 1 [27].

Statistical Analyses

All statistical analyses were performed in the R environment (http://www.r-project.org) unless otherwise indicated. Prior to the statistical analyses, biological data obtained through morphological methods were converted to relative abundance using the “tidyverse” package [28] in R to enhance comparability. Environmental factors were visualized using the “ggplot2” R package [29], and Wilcoxon tests were used to evaluate regional differences by the “ggpubr” R package [30]. The spatial distributions of PP, R and PP/R ratio were constructed using Ocean Data View (version 4.7.4; [31]). Phytoplankton community compositions were visualized at the phylum and genus levels using the top ten in terms of relative abundance and others. Alpha diversity indices we used were species richness, Shannon–Wiener [32] and Pielou’s evenness [33], which were visualized through the “ggplot2” R package [29]. Wilcoxon tests performed with the “ggpubr” R package were used to evaluate differences in alpha diversity indices between three regions [30]. Permutational multivariate analysis of variance (PERMANOVA) was applied to partition the variations of phytoplankton communities among different layers, months, and regions using the “vegan” R packages [34]. Bray–Curtis similarities of phytoplankton communities from 16 stations were used to measure pairwise community similarity [35], which were visualized using the “pheatmap” R package [36]. Spearman’s rank correlations were performed to assess the relationships among PP, R, PP/R ratio, and environmental factors, which were visualized using the “ggcorrplot” R package [37]. Environmental data was standardized in advance using the decostand function in the “vegan” package to improve normality and homoscedasticity [34].

Results

Variations in Environmental Factors

As shown in Table S1 and Fig. S1, the water depth in Meishan bay varied between 1.50 and 9.50 m during the dry month and from 1.60 to 9.40 m during the wet month, while transparency ranged from 0.10 to 4.90 m during the dry month and from 0.60 to 2.18 m during the wet month. Temperature in Meishan bay ranged from 8.42 to 12.89 °C during the dry month and from 28.55 to 30.10 °C during the wet month. A pronounced spatial gradient was observed for salinity and pH, with the ON region displaying significantly higher salinity but lower pH compared to other regions (P < 0.05). Nutrient analysis revealed that surface and bottom concentrations of TIN, nitrate, phosphate, and silicate were consistently highest in the ON during the dry and wet months, while ammonium followed an inverse pattern. Nitrite concentrations remained consistently low (< 0.01 mg/L) across all regions. There is no significant regional variation (P > 0.05) in the surface and bottom concentrations of TC during the dry and wet months, whereas both TOC and DOC were significantly lower in the ON region (P < 0.05). COD concentrations ranged from 0.46 to 3.94 mg/L during the dry month and from 0.21 to 2.30 mg/L during the wet month. Bottom-layer COD in the ON was significantly lower than in other regions during the dry and wet months (P < 0.05). The average concentration of Chl-a during the wet month was more than twice that during the dry month, with the ON region showing significantly lower values than other regions during the dry month (P < 0.05) but no significant spatial differences during the wet month. Moreover, daily mean Chl-a concentrations collected by the long-term monitoring buoy station in Meishan bay during the wet month (Fig. S2) showed that Chl-a concentrations increased rapidly from August 30 (1.29 ± 0.46 μg/L) to September 6 (20.12 ± 2.76 μg/L), then decreased continuously from September 7 (10.87 ± 3.01 μg/L) and reached its lowest value on September 9 (0.62 ± 0.40 μg/L).

Spatial Distributions of Primary Productivity, Respiration Rate, and Production-to-Respiration Ratio

During the dry month, the PP of surface layer ranged from 38.63 to 256.63 mg C m^–3 d^–1, with a mean value of 131.47 mg C m^–3 d^–1, whereas the PP of bottom layer exhibited a broader range (33.30 to 725.94 mg C m^–3 d^–1), averaging 242.45 mg C m^–3 d^–1. The highest PP values of surface layer occurred in the middle of Meishan bay (D5), while the maximum bottom PP occurred near the south dam (DB15, Fig. 2). During the wet month, surface PP showed a substantial increase, ranging from 48.40 to 2164.06 mg C m^–3 d^–1, with an average of 608.50 mg C m^–3 d^–1. The bottom layer PP ranged from 59.05 to 1110.44 mg C m^–3 d^–1, with an average of 588.47 mg C m^–3 d^–1. The highest PP values of surface and bottom layers were observed near the north dam (W2 and WB2, Fig. 2).

Fig. 2.

Primary productivity (mg C m⁻³ d⁻¹) of surface and bottom layers during the dry and wet months

We tested the linearity of the relationship between the INT-F concentration and its absorbance at 485 nm (Fig. S3). The regression line was INT-F = 52.70 absorbance + 0.11, r² = 0.99, n = 7. Then, the ecosystem respiration rate was calculated by applying the standard curve, R/ETS ratio, and RQ value. Figure 3 shows that the R values of the surface layer ranged from 85.99 to 550.35 mg C m^–3 d^–1 during the dry month (mean value = 260.79 mg C m^–3 d^–1), while bottom-layer R ranged from 148.82 to 549.80 mg C m^–3 d^–1 (mean value = 290.56 mg C m^–3 d^–1). Wet month measurements showed significantly higher values, with surface R ranging from 203.62 to 2858.34 mg C m^–3 d^–1 (mean value = 924.57 mg C m^–3 d^–1), and bottom R ranging from 203.84 to 2042.22 mg C m^–3 d^–1 (mean value = 822.35 mg C m^–3 d^–1). Spatially, the highest R values during the dry month were observed in the upper Meishan bay (inside the north dam, stations 3–5 and 7) for both layers. The wet month showed different spatial patterns: the higher R values of the surface layer occurred in the middle of Meishan bay (W8-10), whereas the higher R values of the bottom layer occurred in the WB6, WB7, and WB10 (Fig. 3).

Fig. 3.

Respiration rate (mg C m⁻³ d⁻¹) of surface and bottom layers during the dry and wet months

As a result, the average PP/R ratio of the surface layer was 0.66 during the dry month and 0.98 during the wet month, while the average PP/R ratio of the bottom layer was 0.95 during the dry month and 0.80 during the wet month. Figure 4 shows that the PP/R ratios were only greater than 1 in the lower part of Meishan bay during the dry month (surface and bottom layers of stations 8–11, D14 and DB15). During the wet month, the PP/R ratios were greater than 1 near the north dam (surface and bottom layers of stations 1, 2, and 5, W0, W3, and WB6).

Fig. 4.

Production-to-respiration (PP/R) ratio of surface and bottom layers during the dry and wet months

Phytoplankton Community Composition and Biodiversity

Based on morphological observations, 45 and 69 distinct phytoplankton species were identified in the surface and bottom layers of Meishan Bay during the dry and wet months, respectively. The phytoplankton communities were dominated by two phyla: Bacillariophyta (mean relative abundance, surface layers: 61.18% during the dry month and 86.19% during the wet month; bottom layers: 75.87% during the dry month and 98.35% during the wet month); and Dinophyta (surface layers: 38.75% during the dry month and 12.58% during the wet month; bottom layers: 24.13% during the dry month and 1.60% during the wet month) (Fig. S4). Within Bacillariophyta, the dominant genera included Guinardia, Thalassiosira, Thalassionema, Nitzschia, and Paralia, whereas Dinophyta was primarily represented by Prorocentrum, Karenia, and Gonyaulax (Fig. 5a). In the surface layers during the dry month, the relative abundance of Guinardia decreased from D3 to D15, while Prorocentrum exhibited an opposite trend. During the wet month, Thalassiosira was the most abundant genera at W0, W1, and W2; Karenia dominated at W3 and W5, and Guinardia prevailed at the remaining station. In the bottom layers, the phytoplankton community during the dry month was primarily composed of Guinardia and Prorocentrum, except DB0, DB1, DB2, DB13, DB14, and DB15, while Guinardia accounted for over 60% of the relative abundance at most stations during the wet month, except WB0 (Fig. 5a).

Fig. 5.

a Phytoplankton community compositions at the genus level based on microscopic observation; b phytoplankton abundance (10⁶ cell/L) of surface and bottom layers during the dry and wet months based on microscopic observation

During the dry month, the average phytoplankton abundance was 322,003.13 cells/L and 138,468.75 cells/L in the surface and bottom layers, respectively. The average phytoplankton abundance of wet month was 2,251,593.75 cells/L in the surface layers and 3,668,062.50 cells/L in the bottom layers, which was more than six times that during the dry month (Fig. 5b). According to the cell density threshold for algal bloom outbreaks (cell/L) in the technical specification for algal bloom monitoring in China [38], Meishan bay experienced a mixed phytoplankton bloom of phyla Bacillariophyta and Dinophyta during the wet month. Table S2 shows that the abundance of Guinardia striata was more than its algal bloom threshold (10⁶ cells/L) in the surface layers of W3, W6–W8, W10, W11, W14, W15, and in the bottom layers of WB2–WB11, WB13, and WB15. Leptocylindrus danicus researched its algal bloom threshold (2 × 10⁵ cells/L) at W2 and WB3, while Karenia mikimotoi researched its algal bloom threshold (10⁶ cells/L) at W3 and W5 during the wet month.

By comparing the species richness of phytoplankton communities (Fig. S5), we found species richness of surface and bottom layers during the dry month was mostly less than 10, which was mostly greater than 10 during the wet month. During the dry month, there was no regional difference in the surface Shannon–Wiener index and Pielou’s evenness index, and the bottom Shannon–Wiener index and Pielou’s evenness index were significantly lower in the ON than in the other regions (P < 0.05). During the wet month, the ON region had the highest Shannon–Wiener index and Pielou’s evenness index in both surface and bottom layers, which were significantly higher than those in the MB region (P < 0.05, Fig. S5).

Patterns of Phytoplankton Community Beta Diversity

PERMANOVA analysis (Table S3) revealed significant regional differences in phytoplankton community composition between the ON and MG, as well as between ON and MB in both surface and bottom layers during the dry and wet months (P < 0.05). In contrast, no significant regional differences were detected between MG and MB (P > 0.05). During the dry month, phytoplankton communities exhibited similar spatial patterns between surface and bottom layers. The biggest difference in community compositions across stations was observed in the ON (D0–D2 and DB0–DB2), while the remaining stations exhibited relatively high community similarities (Fig. 6a and b). During the wet month, surface phytoplankton communities exhibited the highest similarity across W6–W15 (excluding W12; Fig. 6c), while bottom phytoplankton communities showed the greatest homogeneity across WB4–WB15, followed by WB1–WB3 (Fig. 6d).

Fig. 6.

Heatmaps illustrating Bray–Curtis similarities of phytoplankton communities based on the taxonomic assignments

Significant Environmental Factors in Structuring Carbon Balance

During the dry month, pH and DOC had a more significant influence on the R of surface and bottom layers than other environmental factors (Fig. 7). The PP of surface layers showed a significant positive correlation with Chl-a concentration, while PP of bottom layers had a significantly positive correlation with transparency and Chl-a concentration. For the PP/R ratio, PP and DOC were the key environmental factors in the surface layer, whereas transparency, TOC, DOC, and COD significantly affected the PP/R ratio of the bottom layer. During the wet month, temperature and Chl-a were the key environmental factors of PP in the surface layer, while Chl-a significantly affected the PP of the bottom layer. No significant correlations were observed between the R of the surface layer and environmental factors, though temperature had a significant effect on the R of the bottom layer. Notably, the PP/R ratio exhibited the strongest positive correlation with PP in both surface and bottom layers (Fig. 7).

Fig. 7.

Correlation analysis among primary productivity (PP), ecosystem respiration rate (R), and production-to-respiration (PP/R) ratio and environmental factors in the surface and bottom layers during the dry and wet months (TIN, total inorganic nitrogen; TC, total carbon; TOC, total organic carbon; DOC, dissolved organic carbon; COD, chemical oxygen demand; Phytoplankton: phytoplankton abundance via microscopic observations) based on Spearman’s rank correlations. The color gradient denotes r-values of Spearman’s rank correlations; a single asterisk (*) indicates a significant P-values of Spearman’s rank correlations (P < 0.05)

Discussion

Ecological Effects of Water Diversion Activities on Phytoplankton Community

Water diversions can induce complex ecological responses, including shifts in aquatic biomass and community composition, bioaccumulation effects, and the introduction of non-native species [1, 39, 40]. In this study, the closure of two dams in Meishan bay during the dry month created stagnant and hydrologically stable conditions in the MG and MB regions. Bray–Curtis similarity analysis of the dry month confirmed that surface and bottom phytoplankton communities exhibited comparable spatial patterns in these regions (Fig. 6). In contrast, the ON region was directly connected to the East China Sea and was affected by it. PERMANOVA analysis confirmed significant differences in phytoplankton community structure between the ON and the other regions (Table S3). During Typhoon Haikui (wet month), two dams and drainage gates of river channels opened 4 h each day, allowing water from the East China Sea and land-based river channels to flow from the north dam to the south dam. Bray–Curtis similarity analysis revealed greater homogeneity in phytoplankton communities across Meishan Bay during the wet month, particularly at stations 4–15 (Fig. 6). One reason was artificial water diversion activities could enhance hydrological connectivity and the transport of aquatic organisms [12, 41]. Another reason was water diversions triggered a mixed diatom–dinoflagellate bloom among stations 2–15 (Table S2), significantly altering the spatial distribution and abundance of phytoplankton compared to the dry month. Notably, phytoplankton abundance in both surface and bottom layers during the wet month exceeded those of the dry month by more than sixfold (Fig. 5b). The prevailing conditions for algal bloom include rainfall, high temperatures, feeble winds, and sluggish water flow [42]. Water diversions introduced heavy rainfall and East China Sea water into Meishan bay, leading to increased salinity in the MG and MB, along with elevated nutrients (e.g., TIN and nitrate) and organic pollutants (e.g., TOC and DOC) compared to the dry month (Fig. S1). Previous researches have demonstrated that abrupt salinity fluctuations can trigger algal blooms [43], while nutrient enrichment accelerates phytoplankton community turnover [44].

Carbon Balance Affected by Water Diversions

Over the past two decades, oceanographic community has intensively studied the carbon balance of ocean [45]. Carbon balance refers to the relationship between PP and R, serving as a measure to investigate the potential input of external organic carbon to the plankton community and to elucidate the coastal function as a potential source or sink of carbon dioxide [46]. Some studies suggested that coastal systems were net heterotrophic due to the delivery of large amounts of continentally derived organic matter [47], while others found that globally the coastal ocean is autotrophic, exporting 15% of its net community production to the open ocean [48]. Rabouille et al. [49] further proposed that the various human-induced modifications of the coastal carbon cycle had resulted in decreased autotrophy of the coastal ocean. In Meishan bay, the average PP/R ratio of the surface layer was 0.66 during the dry month and 0.98 during the wet month, while the average PP/R ratio of bottom layer was 0.95 during the dry month and 0.80 during the wet month (Fig. 4). These results suggested that this coastal ecosystem was a net heterotrophic ecosystem in the whole water column, despite significant shifts in phytoplankton community structure induced by artificial water diversion and associated algal blooms. As observed in previous studies [50–52], a robust association existed between the PP/R ratio and PP. Our investigation further supported these findings, revealing significant correlations between the PP/R ratio and PP, while indicating the lack of correlation between the PP/R ratio and R (Fig. 7). These results strongly suggest that changes in PP, but not in R, control the carbon balance of the semi-enclosed bay.

PP refers to the production of organic compound by phytoplankton [53]. Average values of PP during the wet month were 608.50 mg C m^–3 d^–1 in the surface layer and 588.47 mg C m^–3 d^–1 in the bottom layer, which were more than two times that during the dry month. Figure 7 showed that Chl-a concentration was the most important determinant of surface and bottom PP during the dry and wet months; in addition, transparency had a strong correlation with bottom PP during the dry month. It is unsurprising given the importance of phytoplankton biomass and light availability in regulating primary production [54]. There were other possible causes for the observed increase in PP during the wet month: the inflow of East China Sea in the ON increased the PP of Meishan bay following water diversion activities. Historical seasonal research on the East China Sea has shown that the PP has historically been higher in the summer and autumn due to algal blooms [55]. This is consistent with the results of Fig. 2 that the highest PP values occurred in the ON, which were much higher during the wet month than the dry month.

The regulatory mechanisms governing planktonic respiration remain incompletely understood. Planktonic respiration tends to covary with planktonic primary production [27], with phytoplankton contributing significantly to community respiration [56], and the metabolism of bacterioplankton tends to covary with primary production [57]. During the mixed diatom–dinoflagellate bloom, R values increased substantially, with average surface-layer R reaching 924.57 mg C m^–3 d^–1 and bottom-layer R reaching 822.35 mg C m^–3 d^–1. These values represented 3.5- and 2.8-fold increases, respectively, compared to dry month values (surface layer: 260.79 mg C m^–3 d^–1; bottom layer: 290.56 mg C m^–3 d^–1). Although R tended to increase with increasing algal biomass, no significant correlation was observed between them (Fig. 7). Instead, Spearman’s rank correlation analysis identified pH and DOC as the primary environmental factors influencing the R of the dry month, while few environmental factors contributed to the R of the wet month (Fig. 7). These findings align with established physiological mechanisms: pH modulates microbial metabolic pathways and enzyme activity [58], thereby influencing respiratory processes; DOC serves as a rapidly cycled substrate for the prokaryotic communities [59] and plays a crucial role in supporting the consumption of carbon [60]. Previous research found aquatic ecosystems received organic carbon from multiple autochthonous and allochthonous sources potentially uncoupling ecosystem respiration from planktonic primary production [61, 62], which may explain the different spatial distribution of PP and R during the dry month.

Supplementary Information

Below is the link to the electronic supplementary material.

Author Contributions

QS and CH conceptualized the study and designed the experiment. QS performed the statistical analysis and wrote the manuscript. QY, YX, LZ and MD participated in the research cruise and collected data. CH approved the final version of the manuscript. All authors contributed to the article and approved the submitted version.

Funding

This work was financially supported by the Ningbo Natural Science Foundation (2022J195), the Ningbo Public Welfare Science and Technology Project (2022S116) and K.C. Wong Magna Fund in Ningbo University (SS).

Data Availability

No datasets were generated or analysed during the current study.

Declarations

Competing interests

The authors declare no competing interests.