Increasing phytoplankton-available phosphorus and inhibition of macrophyte on phytoplankton bloom

STOTEN-21296; No of Pages 10 Science of the Total Environment xxx (2016) xxx–xxx

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Increasing phytoplankton-available phosphorus and inhibition of macrophyte on phytoplankton bloom Yanran Dai, Juan Wu, Xiaohang Ma, Fei Zhong, Naxin Cui, Shuiping Cheng ⁎ Key Laboratory of Yangtze River Water Environment, Ministry of Education, Tongji University, Shanghai 200092, China

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Phytoplankton has its strategies to increasing available P in the water column. • Facilitating the total APA level in water and cooperating with bacteria is an essential strategy. • C. demersum and V. spiralis both had prominent performance on regulating the phytoplankton growth. • C. demersum held more potential on controlling algal density and inhibiting quantum yield.

a r t i c l e

i n f o

Article history: Received 25 June 2016 Received in revised form 1 November 2016 Accepted 1 November 2016 Available online xxxx Editor: D. Barcelo Keywords: Phytoplankton Phosphorus Submerged macrophyte Alkaline phosphatase Bacteria

a b s t r a c t We assembled mesocosms to address the coherent mechanisms that an increasing phosphorus (P) concentration in water columns coupled with the phytoplankton bloom and identify the performance gap of regulating phytoplankton growth between two macrophyte species, Ceratophyllum demersum L. and Vallisneria spiralis L. Intense alkaline phosphatase activities (APA) were observed in the unplanted control, with their predominant part, phytoplankton APA (accounting for up to 44.7% of the total APA), and another large share, bacterial APA. These correspond with the large average concentration of total phosphorus (TP), total dissolved phosphorus (TDP) and soluble reactive (SRP) as well as high phytoplankton density in the water column. The consistency among P concentrations, phytoplankton density and APA, together with the positive impact of phytoplankton density on total APA revealed by the structural equation modelling (SEM), indicates that facilitated APA levels in water is an essential strategy for phytoplankton to enhance the available P. Furthermore, a positive interaction between phytoplankton APA and bacteria APA was detected, suggesting a potential collaboration between phytoplankton and bacteria to boost available P content in the water column. Both macrophyte species had a prominent performance on regulating phytoplankton proliferation. The phytoplankton density and quantum yield in C. demersum systems were all significantly lower (33.8% and 24.0%) than those in V. spiralis systems. Additionally, a greater decoupling effect of C. demersum on the relationship between P, APA, phytoplankton density, bacteria dynamic and quantum yield was revealed by SEM. These results imply that the preferred tactic of different species could lead to the performance gap. © 2016 Elsevier B.V. All rights reserved.

⁎ Corresponding author at: 1239 Siping Road, Shanghai 200092, China. E-mail address: [email protected] (S. Cheng).

http://dx.doi.org/10.1016/j.scitotenv.2016.11.002 0048-9697/© 2016 Elsevier B.V. All rights reserved.

Please cite this article as: Dai, Y., et al., Increasing phytoplankton-available phosphorus and inhibition of macrophyte on phytoplankton bloom, Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.11.002

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Y. Dai et al. / Science of the Total Environment xxx (2016) xxx–xxx

1. Introduction

2. Materials and methods

Excessive nutrients from point and diffuse pollution trigger phytoplankton blooms in numerous freshwater systems (rivers, lakes and reservoirs), which create hypoxic “dead zones” in the water body and taint drinking water (Bennett et al., 2001; Carpenter, 2008). So far, a wealth of effort has been devoted to disclosing the relationships between phytoplankton blooms and nutrients, and finding efficient ways to mitigate eutrophication (Trimbee and Prepas, 1987; Ahlvik et al., 2014). However, we are still plagued by the problem. Phosphorus (P) as a common limiting nutrient plays a more important role than nitrogen (N) in the proliferation of bloomforming ‘nuisance’ algae (Correll, 1998; Carpenter, 2008). Unlike N having high losses to the atmosphere related to denitrification, P is widely observed in the more efficient recycling in waters (Nixon et al., 1981). Using a 37-year whole-lake nutrient manipulation experiment in one Canadian lake, Schindler et al. (2008) found P inputs to directly control phytoplankton blooms, and the blooms were even more exacerbated when N inputs were decreased without simultaneously dwindling P inputs. This emphasized the importance of controlling P concentration for preventing algal blooms. In practice, despite considerable attempts to control the external P input, the internal loading of P in waters has become another stumbling block, not least in the anoxic hypolimnion of eutrophic lakes (Cyr et al., 2009; Spears et al., 2012), and the relevant manipulating approach is lacking. In addition, phytoplankton can excrete extracellular alkaline phosphatase enzyme (APA) to hydrolyse organic P source for supplying PO34 − for uptake (Young et al., 2010). Harke et al. (2012) identified the expression of the genes involved in the hydrolysis of phosphomonoesters (phoX) and high affinity P-transport (pstS and sphX) in Microcystis aeruginosa were regulated by external dissolved inorganic phosphorus (DIP) concentration. Some bacteria can also decompose the unavailable organic P into DIP, and thereby contribute to phytoplankton blooms (Zhao et al., 2012). Since the earliest studies (Yount, 1964; Scheffield, 1967), evidence has amounted to state that macrophytes have a strong negative impact on phytoplankton biomass increase and are important for maintaining a clear-water state (De Backer et al., 2012; Dai et al., 2014). Despite it has been documented that macrophyte can inhibit algal growth directly by competing for resources (nutrient and light), excreting allelopathic substances (harmful to algal growth) and altering hydraulic conditions (lower turbulence intensity) (Van den Berg et al., 1998; Mulderij et al., 2007). Macrophyte provides shelter for zooplankton and juvenile fish as well as habitat for macroinvertebrates, which could facilitate the lessening of phytoplankton species richness through grazing (Muylaert et al., 2010). Furthermore, they can reduce sediment resuspension and reinforce sedimentation, which can contribute to controlling the release of internal P loading (Horppila and Nurminen, 2003; Schulz et al., 2003). In recent decades, macrophytes have been widely used for the ecological restoration to maintain a ‘clear’ state, and their efficiencies in controlling nutrients (especially P) dynamics in different waters were examined and optimized (Shilton et al., 2012; Moore et al., 2016; Zhang et al., 2016). Yet, our knowledge about how macrophyte could inhibit phytoplankton bloom to some extent is still limited, particularly as regards how they may interfere with the phytoplankton phosphorusacquisition process. In the present study, we set up several mesocosms with two widespread submerged macrophyte species in China, Ceratophyllum demersum L. and Vallisneria spiralis L. They grow fast and can easily develop into dense stands. Both of them yielded good results in improving water quality despite their morphological distinction (Dai et al., 2012; Qiu et al., 2001). With these mesocosms, we aim to address the mechanisms of phytoplankton to enhance the available phosphorus in water columns, as well as achieve a greater understanding of how macrophyte regulate the phytoplankton growth and identify the performance gap between different macrophyte species.

2.1. Experimental mesocosms A total of 9 PVC tanks (length × width × height: 0.6 m × 0.5 m × 0.8 m) were used for simulation of the lake system and comparative analyses. A 10-cm layer of sediment was placed in each tank, and then the tank was filled with water. This process caused a large amount of sediment resuspension, but based on the observation, it took roughly 3–4 days for most of the suspended particles to settle down in the systems. Sediment used in these mesocosms was collected from the top 0–10 cm of sediment in a eutrophic landscape river flowing through Tongji University, Shanghai. The whole sediment was thoroughly mixed in an open container prior to the experiments. Sediment content of total phosphorus (TP), organic phosphorus (OP), inorganic phosphorus (IP), total nitrogen (TN) and organic matter (OM) was 1.15 ± 0.04, 0.91 ± 0.02, 0.24 ± 0.03, 1.50 ± 0.06 and 55.63 ± 2.07 g/kg on dry weight (DW) basis, respectively. The overlying water was also directly taken from the same river at 0.5 m depth. The concentration of TP, total dissolved phosphorus (TDP), soluble reactive phosphorus (SRP), particulate phosphorus (PP), TN, ammonia nitrogen (NH+ 4 -N), chemical oxygen demand (COD) was 0.487 ± 0.001, 0.41 ± 0.00, 0.38 ± 0.00, 0.07 ± 0.03, 4.92 ± 0.19, 4.14 ± 0.06 and 14.37 ± 0.90 mg/L, respectively. Submerged macrophyte (C. demersum and V. spiralis) were both collected in June 2014 from Donghu Lake in Wuhan. C. demersum, a freefloating submerged species, has fluffy, filamentous, bright-green leaves; V. spiralis, a rooted submerged species has narrow, linear leaves. They were pre-incubated for about 4 weeks in a bigger tank with the same water and sediment as the mimic systems. After removing the adherent water on plants with a line wedge of bibulous paper, these two macrophyte species were evenly planted in three tanks (about 0.50 kg fresh weight per square meter; C. demersum: ~25 plants; V. spiralis: ~20 plants) immediately after filling water, respectively. This experiment manipulated three experimental treatments (planted with C. demersum; planted with V. spiralis; unplanted control), with three replicates for each. All the mesocosms were exposed to natural sunlight in an open room with the transparent roof. During the experimental period, the air temperature ranged from 20.0 °C to 38.0 °C. An appropriate amount of tap water was added periodically (2–4 days) to maintain the initial water level. 2.2. Sampling procedure The study was carried out from 7 July to 4 October (90 days) in 2014. Overlying water samples (0.2 m) were collected every 2–8 days, with a relatively intensive sampling frequency in the first half of the experiment. All the samples taken to the laboratory were analysed or preserved immediately. 2.3. Chemical analysis TN, NH+ 4 -N, and TP of water samples were analysed according to Standard Methods in Environment Monitoring of China (National Bureau of Environment Protection, 2002). COD was measured using a spectrophotometer (DR/2800, Hach Co., Loveland, CO, USA). A fraction of each water sample was filtered with a 0.45 μm pore size hydrophilic Polyether sulfone (PES) membrane, and then determined for TDP using the same measurement as that for TP. SRP was determined according to Murphy and Riley (1962). After the estimation of TP, TDP and SRP, it was then possible to calculate the dissolved organic phosphorus, DOP = TDP − SRP, and the particulate phosphorus, PP=TP− TDP. Sediment samples were all naturally air-dried and sieved with a standard 100-mesh sieve. P fractions were determined using the SMT protocol (Ruban et al., 1999). The TP concentration in sediments was determined by the ascorbic acid method after igniting the sediment at

Please cite this article as: Dai, Y., et al., Increasing phytoplankton-available phosphorus and inhibition of macrophyte on phytoplankton bloom, Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.11.002

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450 °C for 3 h and extracting it by 20 mL of 3.5 M HCl for 16 h. Inorganic phosphorus (IP) was measured with supernatant by the method described above after extracting the sediment with 20 mL of 1 M HCl for 16 h. Organic phosphorus (OP) was calculated by taking the difference between TP and IP. Organic matter (OM) was measured as the weight of the dry sediment after heating in a muffle furnace at 550 °C for 3 h. TN was measured as nitrate by alkaline persulfate oxidation digestion (Lachat Method 12-107-04-1-B, Milwaukee, WI). 2.4. Phytoplankton density and quantum yield Water samples for phytoplankton counting were preserved in situ with acetic Lugol's solution (Parsons et al., 1984). These water samples were placed in the lab for 48 h to allow phytoplankton cells to settle, and then the upper water was removed and the remnant was concentrated to 10 mL. Regarding concentrated samples, phytoplankton was counted using an Olympus microscope (BX53, Olympus, Tokyo, Japan) at a magnification of 400 times after complete mixing. In quantum yield of photosystem II measurements, after the dark adaption, minimum fluorescence was measured using a modulated light source. All water samples (minimum 15 min dark adaption) were accomplished with a Water-PAM (Walz, Effeltrich, Germany). At least five replicate measurements were performed for each sample. 2.5. Extracellular alkaline phosphatase activity Extracellular alkaline phosphatase activity (APA) in the water samples was measured using a procedure modified from Gage and Gorham (1985) and Boon (1989). For the determination of APA, triplicate 5 mL water samples were mixed with Tris–HCl buffer (pH = 8.5, final concentration 13 mmol/L), Na3N (final concentration 5 mmol/L), and p-nitrophenyl phosphate (pNPP, final concentration 0.3 mmol/L), and then incubated at 37 °C for 4 h. Afterward, the absorbency of pnitrophenol was determined spectrophotometrically at the wavelength of 410 nm. Total APA (TAPA) was determined with unfiltered samples and additionally, APA in the filtrate filtered through 3 μm (APAb3) and 0.45 μm (APAb0.45) membrane filters were also analysed, with the latter one representing the soluble APA (SAPA). The contribution of APA to the coarser (APAN3) and finer (APA0.45–3) fractions were calculated as follows: APAN3 = TAPA− APAb3 and APA0.45− 3 = APAb3 − APAb0.45 (Chróst et al., 1984), which are conventionally considered as phytoplankton APA (PAPA) and bacterial APA (BAPA), respectively. 2.6. Bacterial density Colony forming units (CFU) counting of phosphate solubilizing bacteria (PSB) and inorganic phosphorus-degrading bacteria (IPB) in water samples was conducted after incubated at 28 °C for 48 h on their different standard medium described as below (Nautiyal, 1999). Phosphate solubilizing bacteria (each 1 L water): glucose, 10 g; NaCl, 0.3 g; (NH4)2SO4, 0.5 g; yeast extract 0.5 g; MgCO3, 0.3 g; KCl, 0.3 g; MnSO4, 0.03 g; FeSO4, 0.03 g; agar, 15 g; lecithin, 0.2 g; pH, 7.3 ± 0.2. Inorganic phosphorus-degrading bacteria (each 1 L water): glucose 10 g,CaCO3, 5 g; NaCl, 0.3 g; (NH4)2SO4, 0.5 g; yeast extract, 0.5 g; MgCO3, 0.3 g; KCl, 0.3 g; MnSO4, 0.03 g; FeSO4, 0.03 g; agar, 15 g; pH 7.3 ± 0.2. 2.7. Statistical analysis We conducted fitting of the logistic equation to the concentration data of TP, TDP and SRP in water measured from the experimental day 15 to day 82, to explore the temporal pattern of these main phosphorus types and compare the difference between the two treatments and the control (Fig. 1B, Fig. 1D and Fig. 1F). The repeated-measures ANOVA were performed on each of the target variables to test the difference among the two planted treatments and the control, as well as the

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different variables, and the means of these three groups were compared with the LSD method. We conducted the structural equation modelling (SEM) for each of the three groups to explore the relationship among biotic and abiotic variables and how the presence and identity of the macrophyte alter it. Before choosing the parameter estimation method, we first tested the normality of the experimental data. Our data didn't follow multivariate normal distribution, and thus the traditional estimation method such as the maximum likelihood estimation, which assumes the normality of data, is not proper here. To overcome the non-normality of variables, the Bollen-Stine bootstrapping method (BSBM) was used to select and test the fitting model (Bollen and Stine 1992). The goal of an appropriate model is its BSBM p value larger than 0.05 (Bollen and Stine, 1992; Kim and Millsap, 2014). For all the SEM fitting with BSBM, 300 times resampling were conducted, and all the models converged successfully. Our initial model was based on the potential correlation and causality between the biotic and abiotic variables including phytoplankton density, TAPA, PAPA, BAPA, SRP, quantum yield, IPB, PSB (Fig. S1)·Our aim of SEM is, not to select the most parsimonious model, but to choose the model with a good fitting while conserve most paths, to have a comprehensive understanding of the relationship between variables and its changing. Therefore, we gradually removed the relationship with less correlation coefficient until the BSBM p value of all the three groups is larger than 0.05 (Fig. 5). Besides the test by BSBM, we also applied several other commonly used SEM test methods to the finally selected model by BSBM, and results of these methods proved the model has a good or at least acceptable fitting for our data (Table S1). All the analysis and graphing were accomplished with R version 3.2.1 (R Core Team, 2015) and R package “lavaan” (Rosseel, 2012; R Core Team, 2015) was used in the estimation of SEM. 3. Results 3.1. Water column phosphorus concentrations (TP, TDP, SRP, DOP, PP) From day 0 to day 15, all the systems were in the turbulent stage, that is, the resuspended sediment particles were in the process of settling down; macrophyte, phytoplankton and other biotic community were in the adaption phase, and thus concentrations of all forms of P in the overlying water showed a sharp decrease (Fig. 1). After day 82, in the unplanted control all the P concentrations displayed a sharp reduction, which is related to the extensive death of phytoplankton. During the stable stage (day 15 to day 82), the concentration of TP, TDP and SRP all presented a regular and similar temporal pattern, although their values in the control were significantly higher (~4 fold) than those in systems with planted macrophyte (P b 0.05, Fig.1, Table 2). Furthermore, they all changed with time following S-shaped logistic curves and were remarkable higher than that of original water after day 34. As SRP accounted for the most part of TP in the overlying water, there was little visual difference between the fitted curves of TP, TDP and SRP in each group (Fig. 1). Following the early turbulent stage, in all systems the ratio of SRP to TP maintained a high level, and the mean of ratio values was 0.60, 0.66 and 0.80 for the system with V. spiralis, C. demersum and the control group, respectively. There was significant difference in the ratio value between the V. spiralis group and the control (P b 0.05, Table 2). In contrast with TP, TDP and SRP, the concentration of PP and DOP showed temporally irregular pattern during the experimental period (Fig. 1). For PP, after day 15 and when the systems were in natural state characterized by relatively slight changes of most variables, the concentration in the control group was noticeably higher than that in both treatment groups (P b 0.05; Fig. 1, Table 2). But for DOP, no significant difference was detected between the control and experimental treatments, though the mean value in the system with V. spiralis was 2-fold higher than that of the C. demersum group.

Please cite this article as: Dai, Y., et al., Increasing phytoplankton-available phosphorus and inhibition of macrophyte on phytoplankton bloom, Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.11.002

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Y. Dai et al. / Science of the Total Environment xxx (2016) xxx–xxx

Fig. 1. Concentration dynamics of various forms of phosphorus including (A) TP, (C) TDP, (E) SRP, (G) DOP and (H) PP in treatments with macrophyte and the control over the experimental period. Values are means ± SD. Smooth curves in (B), (D) and (F) demonstrate fitting logistic equations to data from day 15 to day 82 of this experiment (shaded area in (A), (C) and (E)). The coefficient of determination, R2, of these fittings: (B) 0.976, 0.950, 0.669 for the control and treatments with C. demersum and V. spiralis, respectively; (D) 0.979, 0.943, 0.684 for the control and treatments with C. demersum and V. spiralis, respectively; (F) 0.979, 0.942, 0.674 for the control and treatments with C. demersum and V. spiralis, respectively.

Additionally, repeated-measures ANOVA indicates that P concentration in the overlying water changed significantly with the time and was strongly affected by submerged macrophyte growth (P b 0.05, Table 1). 3.2. Temporal variation in the density and quantum yield of phytoplankton Different growth pattern of phytoplankton in experimental systems was observed, depending on the presence and identity of macrophyte (Fig. 2). Although there were obvious fluctuations in all groups throughout the experiment, the average value of phytoplankton density in the two treatment groups were significantly lower than that of the unplanted control. And the pronounced difference was also observed between the two treatments (P b 0.05, Table 2). The quantum yield of phytoplankton was significantly greater in the control group (0.30), followed by the V. spiralis (0.25) and C. demersum (0.19) treatment (Table 2). 3.3. Temporal variation of bacterial density Due to the disturbance induced by filling the mesocosms with water, a large amount of sediment particles were re-suspended into overlying

water and triggered a dramatic increase in density of PSB and IPB. But there was a sharp decline afterward. For PSB, its density showed a decline in all systems lasted until day 15 and then stayed at a relatively steady state, and the temporal variation within the first 15 days was similar, except the higher level in the control; while the IPB density displayed several subsequent obvious peaks after the first sharp falling, especially in the control (Fig. 3). The highest mean value of bacterial density was found in the control group, with values of 12.2 × 10 CFU/ mL (PSB) and 15.8 × 10 CFU/mL (IPB), which were both significantly higher than those in planted groups (P b 0.05, Table 2). No pronounced difference was observed between the two treatments (P N 0.05). 3.4. Temporal variation of extracellular phosphatase activity After entering the stable stage, an obvious discrepancy appeared in phosphatase activities among different systems, especially the total APA. The LSD results indicated significant differences in total APA between macrophyte groups and the control group, with the highest value detected in the control, followed by the V. spiralis and C. demersum group (P b 0.05, Table 2). Furthermore, the soluble APA

Please cite this article as: Dai, Y., et al., Increasing phytoplankton-available phosphorus and inhibition of macrophyte on phytoplankton bloom, Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.11.002

Y. Dai et al. / Science of the Total Environment xxx (2016) xxx–xxx Table 1 Summary of results of repeated measures ANOVA for all variables of water columns (group: one planted with C. demersum, one with V. spiralis, and the unplanted control; TP: total phosphorus; TDP: total dissolved phosphorus; SRP: total dissolved phosphorus; DOP: dissolved organic phosphorus; PP: particulate phosphorus; Ratio: SRP to TP; TAPA: Total extracellular alkaline phosphatase activity; SAPA: soluble APA; BAPA: bacterial APA; PAPA: phytoplankton APA; IPB: inorganic phosphorus-degrading bacteria; PSB: phosphate solubilizing bacteria). Variable

TP TDP SRP DOP PP Ratio TAPA SAPA BAPA PAPA IPB PSB Phytoplankton density Quantum yield

Time

Time*group

F

P

F

P

13.0 28.9 26.2 5.88 23.3 40.9 59.2 15.7 15.5 54.7 21.1 119 11.3 28.4

0.000 0.000 0.000 0.009 0.000 0.000 0.000 0.000 0.021 0.004 0.000 0.000 0.000 0.000

8.45 14.0 13.1 1.75 2.07 1.80 1.21 3.49 1.83 1.01 2.41 6.12 3.72 2.06

0.001 0.000 0.000 0.017 0.015 0.097 0.306 0.000 0.077 0.461 0.001 0.000 0.000 0.000

Significant differences at P b 0.05 are displayed in bold.

results for the control increased during the stable stage, while they stayed relatively constant for the other results, and predominantly lower value was detected in the system with C. demersum compared to the control; the phytoplankton and bacterial APA value in the unplanted control were all significantly higher than treatments. The contribution of the phytoplankton APA to total APA was 47.0%

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(C. demersum), 46.2% (V. spiralis), and 44.7% (control), respectively, all significantly higher than the other two components (soluble APA and bacterial APA; P b 0.05).

3.5. Structural equation modelling We conducted SEM to explore the relationship between biotic and abiotic variables and how its change depends on the presence and identify of macrophyte. SRP concentration in the control model had the strongest negative impact on phytoplankton APA and bacterial APA with a standardized partial regression coefficient of − 0.52 for both (P b 0.05), followed by the impact on quantum yield with the coefficient of −0.46 (P b 0.05). The coefficient value from phytoplankton density to total APA was 0.21 (P b 0.05). Additional significant positive covariance terms existed between phytoplankton APA and bacterial APA, as well as between IPB and PSB (P b 0.05). For two treatments, distinct relationship patterns between variables were estimated from that of the control group. In the C. demersum model, only significant negative impact of SRP on quantum yield was detected, and its coefficient value was − 0.28 (P b 0.05); while in the V. spiralis model, SRP concentration strongly influenced quantum yield, bacterial APA and phytoplankton APA (P b 0.05), with the coefficient of −0.51, −0.32 and −0.23, respectively. Additionally, the significant coefficient of 0.28 from phytoplankton density to total APA was also found in this model (P b 0.05). Similar to the control model, there were significant positive covariance terms between phytoplankton APA and bacterial APA, together with that between IPB and PSB in the model of V. spiralis (P b 0.05), yet in the C. demersum model only covariance relation between IPB and PSB was found.

Fig. 2. Dynamics of (A) quantum yield and (B) phytoplankton cell density in treatments with macrophyte and the control over the experimental period. Values are means ± SD.

Please cite this article as: Dai, Y., et al., Increasing phytoplankton-available phosphorus and inhibition of macrophyte on phytoplankton bloom, Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.11.002

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Table 2 Repeated-measures ANOVA between groups based on means for variables (LSD) in water columns. (Analysis data: from day 2 to day 90). Variable

C. demersum

V. spiralis

Control

TP (mg/L) TDP (mg/L) SRP (mg/L) DOP (mg/L) PP (mg/L) Ratio Phytoplankton density (×104 cells/mL) Quantum Yield IPB (×10 CFU/mL) PSB (×10 CFU/mL) TAPA (nmol/L·min−1) SAPA (nmol/L·min−1) BAPA (nmol/L·min−1) PAPA (nmol/L·min−1)

0.16b 0.14b 0.13b 0.01 0.02b 0.66ab 3.70a 0.19a 8.96b 9.91b 6.68b 2.15a 1.83b 3.14b

0.14b 0.12b 0.10b 0.02 0.02b 0.60a 5.59b 0.25b 10.1b 9.34b 8.19b 2.15ab 2.28b 3.78b

0.57a 0.53a 0.51a 0.02 0.05a 0.80b 10.1c 0.30c 15.8a 12.2a 11.8a 3.96b 3.10a 5.28a

Values with different letter are significantly different, P b 0.05.

4. Discussion 4.1. Mechanisms behind the increasing phytoplankton-available phosphorus in water columns Presently, the foci on phytoplankton blooms are still on the level and dynamics of P, along with how to control its cycling in aquatic systems (Carpenter, 2008; Baken et al., 2014). Our intensive monitoring of all forms of P in the water column reveals that the concentration of TP,

TDP and SRP concentration followed a logistic-function pattern, however, the phytoplankton density showed frequent fluctuations rather than the sustained growth. This is dissimilar to the result found by of Cao et al. (2007) that the increase in phytoplankton density was almost synchronous with the rise of SRP concentrations in Lake Taihu. On account of the phenomenon that many dead and fragmentary phytoplankton cells were observed under the microscope, increased interspecific competitions and high grazing rate in the mimic system may be the main reason leading to the difference, but the specific cause needs to be explored further. It is noteworthy that after day 34 the concentrations were all far higher than that of original water. Additionally, at the end of the experiment, sediment TP content was 1.09 g/kg DW in the control, significantly lower than that in original sediment (1.15 g/kg DW). Therefore, we can infer that there was a mass of internal sediment release of SRP. Normally, the SRP diffusive flux from the sediment largely depends on the SRP concentration difference between the pore water and overlying water (Søndergaard et al., 1992). However, an extraordinary phenomenon was detected in this study, which was also found by Xie et al. (2003) that there was a persistent coincidence between the occurrence of Microcystis blooms and the increase of both TP and SRP concentrations in the water. Although the high pH was deduced as the possible factor to mediate the enhanced release of P from sediments at shallow eutrophic lakes, they insisted that there are some other mechanisms. It has been well documented that phytoplankton could motivate the production of available P through excreting membranebound phosphatase to hydrolyse organic P into SRP and benefit from bacterioplankton and zooplankton which could also contribute to the APA in waters (Jansson, 1976; Lomas et al., 2010; Young et al., 2010;

Fig. 3. Dynamics of two groups of bacteria which are related to the phosphorus cycle (A) phosphate solubilizing bacteria and (B) inorganic phosphorus-degrading bacteria in treatments with macrophyte and the control over the experimental period. Values are means ± SD.

Please cite this article as: Dai, Y., et al., Increasing phytoplankton-available phosphorus and inhibition of macrophyte on phytoplankton bloom, Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.11.002

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Fig. 4. Various alkaline phosphatase activities including (A) total APA, (B) soluble APA, (C) phytoplankton APA and (D) bacterial APA in treatments with macrophyte and the control over the experimental period.

Artigas et al., 2012). Our results show that phytoplankton APA (mean value of 5.28 nmol/L·min−1), as the predominant component, accounted for 44.7% of the total APA, significantly higher than that of bacterial APA (mean value of 3.10 nmol/L·min−1). These observations are closely analogous to the results of Currie et al. (1986) and Labry et al. (2005), who observed that up to 44% and 57–63% of the total APA was associated with phytoplankton, respectively. Furthermore, the significant positive influence of phytoplankton density on total APA was detected by SEM (Fig. 5A). Consequently, excreting membrane-bound APA in water may be an essential strategy for phytoplankton to increase the available P. Similar to previous findings (Young et al., 2010), the SEM also revealed that elevated SRP concentration could in turn suppress phytoplankton APA (Fig. 5A). However, there was also a constitutive phytoplankton APA, which was maintained even after prolonged exposure to nearly 1.30 mg/L and saturation of internal P pools (Fig. 1, Fig. 4C). Another interesting phenomenon is that although the phytoplankton APA declined more pronouncedly with increasing TP, TDP and SRP concentration at the first half of natural stage when the concentrations were below ~ 0.8 mg/L, the APA levels were all obviously higher than that at the later stage. This might be related with

the different phytoplankton growth rate and the corresponding craving for P. Additionally, the same partial regression coefficient of − 0.52 was achieved from SRP to bacterial APA, demonstrates that there was no preference for SRP to suppress phytoplankton APA or bacterial APA under this circumstance. The positive interactive effect between algal and bacterial APA in the unplanted control system, indicates phytoplankton cooperated with bacteria to increase available P in this study, and this could be further supported by the significant partial regression coefficient of 0.21, from phytoplankton density to total APA but not for phytoplankton APA. Previous studies also concluded that natural bacteria could contribute to phytoplankton blooms in the water (Zhao et al., 2012). Quantum yield as an indication of the amount of energy used in photochemistry by photosystem II, has aroused considerable attentions (Samori et al., 2013). The previous researches indicate that quantum yield levels of algae could vary with environmental stresses, among which P limitation is an essential one (Geider et al., 1998; Lippemeier et al., 2003). As Fig. 5A illustrated, SRP had significant negative impact on quantum yield. This could be associated with interspecific competition in phytoplankton communities, including nutrient and light, caused by the excessive growth.

Please cite this article as: Dai, Y., et al., Increasing phytoplankton-available phosphorus and inhibition of macrophyte on phytoplankton bloom, Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.11.002

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Fig. 5. Structural equation model explaining relationships between phytoplankton, bacteria, phosphorus and alkaline phosphatase activities in (A) the control, (B) the group with C. demersum and (C) the group with V. spiralis. Solid lines represent direct causal pathways, while dashed lines link the paired covariates. Significant pathways are indicated with bold lines and asterisks.

4.2. Inhibition of submerged macrophyte on phytoplankton bloom-forming In this study, the two species of submerged macrophyte, C. demersum and V. spiralis both grew well in the system with the fresh biomass of 0.813 ± 0.12 kg and 0.746 ± 0.10 kg at the end of the experiment, and had prominent performance on regulating the phytoplankton growth (Fig. 2, Table 2). However, it seems to be that C. demersum had more potential than its counterpart as the phytoplankton density and quantum yield in systems with C. demersum were both kept significantly lower (33.8% and 24.0%) than those in the system with V. spiralis. To explore the underlying mechanism of macrophyte regulating phytoplankton biomass, and to address the performance gap between different species, we paid close attention to the variation of different forms of P in the overlying water, and found, even though the TP, TDP and SRP concentration were all adequately represented by the logistic model, there was dramatic differences between systems with and without macrophyte. Moreover, significantly higher PP concentration in control was also detected. These manifest as C. demersum and V. spiralis having the remarkable ability to maintain P at a substantially low level in water columns. It is a widely accepted concept that phytoplankton density and biomass are significantly related to P concentration in the water, and phytoplankton biomass (chlorophyll) responds in a nonlinear, sigmoidal fashion with increasing phosphorus levels among lakes (Watson et al., 1992; Trevisan and Forsberg, 2007; Zimmerman and Cardinale, 2014). Nonetheless, there was no strong

facilitated influence from SRP to phytoplankton density in the systems (Fig. 5), indicating that submerged macrophyte had prominent effect on the relationship between SRP and phytoplankton growth. Various mechanisms for submerged macrophytes to inhibit the phytoplankton growth have been documented (Carpenter and Lodge, 1986; Hilt, 2015), among which the allelopathy and competing for nutrients are the main ones (Hilt and Gross, 2008). Yet different species have distinct tactics, largely dependent on their morphology. Most aquatic angiosperms are rooted and obtain the majority of their macronutrients from the sediment (Carignan and Kalff, 1980), V. spiralis chosen in our study falls into this category; C. demersum, however, with only its rhizoids attached to the sediment, its nutrient intake occurs primarily over the shoots (Denny, 1987), and therefore, competes with phytoplankton for nutrients. Furthermore, C. demersum usually occupy most of the water column, and hence dramatically decrease the light availability for phytoplankton (Mjelde and Faafeng, 1997). In the present study, there was no significant difference between the two species in the ability of maintaining the low P concentration in the water column (Fig. 1, Table 2). The total APA, phytoplankton APA and bacterial APA in both systems with macrophyte were significantly decreased, compared to the control. This is consistent with the results of Zhou et al. (2000), who observed that the concentration of orthophosphate was significantly lower, coupled with the decreasing function of organic P hydrolysis, in terms of lower APA in water. All these indicate that macrophytes may contribute to the retention of the available P in water, with respect to APAs

Please cite this article as: Dai, Y., et al., Increasing phytoplankton-available phosphorus and inhibition of macrophyte on phytoplankton bloom, Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.11.002

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mediated P cycling. The APAs were, in general, similar between macrophyte groups, although the value was lower in the C. demersum system, suggesting that there was no obvious difference between the two species of macrophyte regarding their direct influence on APA. The validation with variations of phytoplankton density, quantum yield and P concentration in macrophyte systems, along with the fact that phytoplankton quantum yield is unrelated with their biomass, imply that the performance gap between the two species was chiefly due to the distinct allelopathy. This conclusion could be supported by the results that the allelopathic activity of essential oils obtained from C. demersum was slightly stronger than that produced by V. spiralis (Xian et al., 2006), and more literatures suggested that C. demersum has remarkable inhibition on the growth of phytoplankton and cyanobacteria (Wium-Andersen et al., 1982; Jasser 1995; van Donk and Gulati, 1995; Gross et al., 2003). Additionally, the relationships between phytoplankton density, SRP, APA, quantum yield and bacterial number were largely weakened due to the macrophyte growth, but with different patterns between the two treatments, further indicating that the two species have distinct tactics to inhibit the phytoplankton blooms. 5. Conclusions A detailed experimental study on the mechanisms behind increasing phytoplankton-available phosphorus in the water column and the inhibition of submerged macrophyte on phytoplankton bloom-formation shows phytoplankton growth induced the increase of TP, DOP and SRP concentration in overlying water following the logistic-curve pattern. Facilitating the total APA level in water and cooperating with bacteria is an important strategy for phytoplankton to increasing the available P. Although prominent performances of two submerged macrophyte species were both found to regulate the algal growth, C. demersum held more potential on controlling phytoplankton density and inhibiting quantum yield than V. spiralis. It may be the distinct allelopathy activities leading to different quantum yield levels and, therefore, the performance gap between the two species. In this research, we enriched the understanding of the dynamics of various P forms in the water body and their relationships with the proliferation of phytoplankton. Besides merely reducing the P concentration, proper interference with the conversion of different P forms (e.g. adding inactive agents), may also help to combat phytoplankton blooms. We also investigated the role of macrophyte in preventing phytoplankton blooms and improving water quality, and highlighted the difference in the performance of different macrophyte. This finding requires a more comprehensive work in searching macrophyte species that suit local circumstances. Acknowledgments This work is supported by the Key Project of National Science and Technology Programme (2012ZX07103-004) and Shanghai Science and Technology Innovation Project (16DZ1204803). We give our deep thanks to Deirdre McClean for the refining the English writing of this manuscript. The valuable comments and suggestions from the two anonymous reviewers were deeply appreciated. Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.scitotenv.2016.11.002. References Artigas, J., Soley, S., Pérez-Baliero, M.C., Romaní, A.M., Ruiz-González, C., Sabater, S., 2012. Phosphorus use by planktonic communities in a large regulated Mediterranean river. Sci. Total Environ. 42, 180–187. Ahlvik, L., Ekholm, P., Hyytiäinen, K., Pitkänen, H., 2014. An economic-ecological model to evaluate impacts of nutrient abatement in the Baltic Sea. Environ. Model. Softw. 55, 164–175.

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Please cite this article as: Dai, Y., et al., Increasing phytoplankton-available phosphorus and inhibition of macrophyte on phytoplankton bloom, Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.11.002