Light-dominated selection shaping filamentous cyanobacterial assemblages drives odor problem in a drinking water reservoir
Abstract
Filamentous cyanobacteria have substantial niche overlap, and the causal mechanism behind their succession remains unclear. This has practical significance since several filamentous genera are the main producers of the musty odorant 2-methylisoborneol (MIB), which lead to odor problems in drinking water. This study investigates the relationships between two filamentous cyanobacteria, the MIB-producing genus Planktothrix and the non-MIB-producing genus Pseudanabaena, in a drinking water reservoir. We firstly identified their niche characteristics based on a monitoring dataset, combined this information with culture experiments and developed a niche-based model to clarify these processes. The results reveal that the optimal light requirements of Pseudanabaena (1.56 mol m-2d-1) are lower than those of Planktothrix (3.67 mol m-2d-1); their light niche differentiation led to a fundamental replacement of Planktothrix (2013) by Pseudanabaena (2015) along with MIB decreases in this reservoir during 2013 and 2015. This study suggests that light is a major driving force responsible for the succession between filamentous cyanobacteria, and that subtle niche differentiation may play an important role in shaping the filamentous cyanobacterial assemblages that drives the MIB odor problems in drinking water reservoirs.
Keywords
2-methylisoborneol (MIB), Planktothrix, Pseudanabaena, filamentous cyanobacteria, succession
1 Key Laboratory of Drinking Water Science and Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences
2 Shanghai Chengtou Raw Water Co. Ltd
3 Department of Biosciences, University of Oslo
4 National Engineering Research Center of China (South) for Urban Water
5 University of Chinese Academy of Sciences
✉ Correspondence: Min Yang <yangmin@rcees.ac.cn>
Introduction
Odor problems in source water caused by 2-methylisoborneol (MIB), a secondary metabolite of filamentous cyanobacteria in many reservoirs and lakes1, have been a common issue in the Northern Hemisphere, and have now been moving southward2–7. The major MIB producers include Oscillatoria8–10, Planktothrix11, Phormidium10, Pseudanabaena12, Lyngbya13 and Planktothricoides14. It should be noted that MIB yield varies among different strains11,15–17, and some strains of the known MIB-producing species are in some cases not even able to produce MIB8,13,18. Nonetheless, MIB occurrences and concentrations are mainly determined by the presence and abundance of MIB-producing filamentous cyanobacteria in the aquatic environment.
Nutrients, water temperature and light are essential factors governing the growth and competition of phytoplankton. Recent studies have emphasized the importance of underwater light condition on their seasonal successions in both field investigations19,20 and numeric models21–23. The cellular projected area (CPA, the two-dimensional area measurement by projecting cell shape on to a plane, as defined in22) has been proposed as a key indicator of cellular light harvesting potential, and the specific CPA (CPA/V, normalized CPA by cell volume) could be used to indicate the optimum light requirements for various species with different cell shapes22,24. For example, the bloom-forming cyanobacteria Microcystis with a low specific CPA requires high light intensity and hence is usually observed in surface water, particularly in the summer period, while filamentous cyanobacteria having a higher specific CPA tend to live in subsurface layers, where light intensity is usually low, but nutrient availability is high25. The low-irradiance-tolerating characteristics of filamentous cyanobacteria have been verified by laboratory culture experiments26 and field investigation27. The light niche differentiation between filamentous cyanobacteria and other phytoplankton enables us to model their succession based on ecological niche modelling25,28–30. However, little is known about the competition between different filamentous cyanobacterial genera, since they are likely to have substantial niche overlap. Therefore, it is desirable to know whether the changes in composition of filamentous cyanobacterial assemblages are deterministic (governed by niche differentiation) or stochastic (dominated by neutral theory).
QCS Reservoir is a newly constructed estuary reservoir used as the major drinking water resource for Shanghai, China. It directly imports highly turbid water from the Yangtze River, leading to underwater light conditions that favors filamentous cyanobacteria rather than Microcystis7, and therefore has suffered from MIB odor problems since it was put into use in 2011. The filamentous cyanobacterium Planktothrix was the main MIB producer according to our previous study7. From 2011 to 2015, MIB concentrations showed a decreasing pattern along with the decrease of Planktothrix cell densities and the increase of another filamentous cyanobacterium, Pseudanabaena. We therefore hypothesize that their competition and succession might have great impact on MIB occurrence in this reservoir. The aim of this study is to identify the driving forces responsible for the filamentous cyanobacterial assemblages, so that it can provide scientific basis to solve the practical MIB problem in drinking water reservoirs. Accordingly, we identified their niche characteristics based on a monitoring dataset together with culture experiments, and developed a niche-based model to clarify these ecological processes.
Results
MIB dynamics in QCS Reservoir
MIB concentration of the river water (inlet) was rather low during the investigation (Fig. S1). Significant seasonal variation of MIB was observed in QCS Reservoir (Fig. 1b); higher MIB concentrations (mean: 49.2 ng L-1, range: 0.5-97.8 ng L-1) were mainly observed during the period June to September (mean: 7.5 ng L-1, range: 0.5-12.3 ng L-1). The long-term development of MIB in June to September between 2011 and 2015 exhibited a significant decrease (Fig. 1c). The mean concentrations in the first year were 101.0 ng L-1 (range: 0.5-257.0 ng L-1), equivalent to 6 times its human olfactory threshold (15 ng L-1,11), and thus aroused great attention. However, in the following two years the mean MIB concentrations decreased to 34.2 ng L-1 (range: 0.5-107.0 ng L-1) and 29.4 ng L-1 (range: 0.5-66.4 ng L-1), respectively. In 2014 and 2015, the concentrations further decreased to 6.2 ng L-1 (range: 0.5-15.6 ng L-1).
Time series analysis of filamentous cyanobacteria
Four main filamentous cyanobacteria were recorded during the investigation in QCS Reservoir (Fig. S2); Planktothrix (30.2%) and Pseudanabaena (30.5%) exhibited higher occurrence frequencies than Phormidium (14.9%) and Lyngbya (2.5%). Lyngbya was only observed for 8 samples, so it was not possible to identify the seasonality. Planktothrix (n = 175), Pseudanabaena (n = 168) and Phormidium (n = 88) were mainly observed during May to October (Fig. S3). Planktothrix was identified as the MIB producer in QCS Reservoir according to our previous study7. Microcystis dominated during August and September, which could affect the growth of filamentous cyanobacteria (Fig. S3). Therefore, Pseudanabaena was considered as the most important competitor to Planktothrix based on their seasonal distribution patterns (Fig. S3) and their habitats.
20.5% of the variances of Planktothrix cell density could be explained by seasonal and long-term trend terms using the GAM model (Eq. 4, Table S7, Fig. S6). The model suggested that the variance of Planktothrix was dominated by strong seasonality (p < 0.0001, Fig. 2a). During the investigation, no Planktothrix were detected in February, March and April; the earliest record of Planktothrix was in May, with the mean density of 6.79 × 104 cell L-1 (0-2.04 × 105 cell L-1, 10%-90% quantile, same hereinafter); the density increased in the following 3 months until late August, with a maximum of 1.01 × 106 cell L-1 (0-3.45 × 106 cell L-1); and subsequently decreased to 8.33 × 104 cell L-1 (0-1.35 × 105 cell L-1) in December and 2.78 × 104 cell L-1 (0-3.89 × 104 cell L-1) in January. Besides, Planktothrix also showed a long-term trend with a declining pattern (p = 0.0915, Fig. 2b). The mean density during July to September decreased by 93% from 1.95-2.42 × 106 cell L-1 in 2011 and 2012 to 1.40 × 105 cell L-1 in 2015.
47.7% of the variance of Pseudanabaena could be explained by seasonal and long-term trend terms (Table S8, Fig. S7), and a similar seasonal pattern (p < 0.0001) of peak concentration (3.36 × 106 cell L-1, 0-1.306 × 107 cell L-1) in early September (Fig. 2c). The long-term changes of Pseudanabaena showed an opposite pattern (p < 0.0001) to Planktothrix; this genus became more abundant after 2014 and has kept increasing since then (Fig. 2d). Noted that, there was an early peak of Pseudanabaena during April and June (Fig. 2c).
Limnological and meteorological characteristics
Fig. 3 shows the temporal distribution pattern of nutrients and meteorological parameters in QCS Reservoir. Nutrients including total nitrogen (TN), nitrate, ammonia and total phosphorus (TP) showed similar seasonality (Table S1, Table S2, Table S3, Table S4), with the lowest concentrations observed in August and September owing to sedimentation losses in the summer period. Regarding the inter-annual dynamics, the TN and nitrate concentrations in 2014 were much higher than those in 2013 and 2015, ammonia showed a declining trend, while TP stayed almost unchanged between years. Precipitation was mainly observed between May to September, highly correlated with air temperature and solar radiation (Table S5 and Table S6. It should be noted that the solar radiation showed a declining trend from 2013 to 2015, possibly owing to the higher precipitation in the later years. Wind speed and relative humidity showed different seasonal patterns from 2013 to 2015.
Ecological Niche modelling of Planktothrix and Pseudanabaena
According to several published research31–33 and our previous culture experiments15,34 and field studies7,11,35, we firstly selected water temperature, light availability, nutrients (including total nitrogen, nitrate, ammonia, total phosphorus), wind speed, and daily maximum air temperature as the potential predictors of cyanobacterial abundance Six predictors were selected including water temperature, light availability, total N, total P, ammonia and wind speed, according to linear models (LM1 and LM2) between these predictors (\(X\)) and Planktothrix (\(Y_1 = log_{10}(N_1 + 1)\), Table S9) and Pseudanabaena (\(Y_2 = log_{10}(N_2 + 1)\), Table S10) associated with backward stepwise selection (Table S11) and Table S12 and variance inflation factor (VIF, Table S13). These predictors were subsequently classified into 4 groups based on the correlation analysis between each two predictors (Fig. S8), which are i) water temperature and light availability (T&I); ii) Total N and ammonia (TN&NH4); iii) Total P (TP) and iv) wind speed (WS). The interactions between the predictors within each group were considered by modelling the interaction between T and I by bivariate tensor-product smoothers. GAM models for abundances of Planktothrix (GAM1, Table S14) and Pseudanabaena (GAM2, Table S15) were fitted with these predictors. The results suggested T&I and TN&NH4 were significantly correlated with the abundance of Planktothrix, while T&I, TP and WS were significant correlated with that of Pseudanabaena.
Based on the results above, the niche models of Planktothrix (GAM3, Table S16) and Pseudanabaena (GAM4, Table S17) were determined using their corresponding key explaining variables. In the temperature and light availability plane, both Planktothrix and Pseudanabaena exhibited greater abundance in high-temperature conditions (>20°C). Pseudanabaena could sustain higher abundance under lower light conditions (0.7-1.5 mol m-2 d-1, p < 0.001) compared to Planktothrix (1.4~2.3 mol m-2 d-1, p < 0.001), as illustrated in Fig. 4a and Fig. 4c. Planktothrix was less abundant in high total N and ammonia conditions (p = 0.007, Fig. 4b). While Pseudanabaena was slightly more abundant in moderate total P (p = 0.064) and moderate wind speed (p = 0.004) conditions.
Growth characteristics of Planktothrix and Pseudanabaena under different light doses
Since Planktothrix and Pseudanabaena exhibited different growth potentials under different light conditions, a culture experiment was performed to investigate the effect of light levels on their growth yield (Fig. 5a). The results suggested that light dose has a large impact: the cell density of Planktothrix in stationary phases (day 15 to 35) increased along with the light dose when it was lower than 3.67 mol m-2 d-1, while the growth was inhibited under higher light dose. The optimum light dose for Pseudanabaena is 1.56 mol m-2 d-1, which was lower than that of Planktothrix. This suggested that photoinhibition exists for Pseudanabaena when the light dose was higher than 3.67 mol m-2 d-1.
Relationship between niche space and temporal trajectories of environmental factors in QCS Reservoir
The focal niche spaces of Planktothrix and Pseudanabaena were determined with the boundary defined by the 90% quantile of predicted abundances. The seasonal trajectories of environmental factors (water temperature & PAR) in QCS Reservoir are illustrated in Fig. 5b. In 2013, the trajectory went through the focal niche space of Planktothrix in July and August, indicating that Planktothrix had an advantage over Pseudanabaena in this year. In the following two years, especially for July and August, the trajectories followed different paths due to the lowered solar radiation and went through the niche space of Pseudanabaena instead. This probably enhanced the competitive ability of Pseudanabaena.
Discussion
Planktothrix was the main MIB producer in QCS Reservoir during 2011 and 2015, as identified in our previous study7, and the synchronous declines of this genus and MIB (Fig. 1, Fig. 2) supported this interpretation. The driving forces responsible for the Planktothrix decline are therefore important for understanding the odor problems of QCS Reservoir.
Another filamentous cyanobacterium, isolated and identified as non-MIB-producing Pseudanabaena, showed an increasing trend during the study period. Both Planktothrix and Pseudanabaena showed the same seasonal patterns in this reservoir; in particular, Planktothrix was more abundant in the first two years while Pseudanabaena was more abundant afterwards (Fig. 2). Filamentous cyanobacteria tolerate low light36, and many studies have shown that they tend to grow in spring and/or autumn seasons8,11,37. In this study, Planktothrix and Pseudanabaena were mainly observed during the summer period (July to September) and did not follow the typical seasonality of filamentous cyanobacteria. We speculate that the unusually low water transparency in the reservoir (~40-60 NTU in turbidity, QC01) creates a habitat with low subsurface water light intensity, which favors filamentous cyanobacteria but is inhospitable to heliophilic Microcystis. In addition, the absence of surface Microcystis will also provide a more favorable underwater light environment for filamentous cyanobacteria25.
Light niche differentiation between filamentous cyanobacteria and Microcystis can explain the competition between them, as reported in a study of Miyun Reservoir25. However, the situation in QCS Reservoir is different, since the MIB-producing Planktothrix has no competition from surface-blooming Microcystis but rather from the ecologically similar Pseudanabaena, during 2013 to 2015. The succession and/or competition between them are difficult to determine due to their niche overlap.
The temporal dynamics of limnological conditions in QCS Reservoir suggests different seasonal and inter-annual patterns (Fig. 3). The presence of nutrients in appropriate concentrations is one fundamental requirement for net primary production and accumulation of phytoplankton biomass, while nutrients have been recently considered to be of limited value and to even be useless to shape the phytoplankton dynamics if we focus on the genus level38. Culture studies have also observed the insignificant effect of nutrient concentrations on the growth of several filamentous cyanobacteria strains including Planktothrix agardhii39 and Phormidium sp.40. Although the nutrients (except TP) exhibited inter-annual changes in QCS Reservoir, the concentrations are generally sufficient to support the observed biomasses of filamentous cyanobacteria. The 15th Workshop of the International Association for Phytoplankton Taxonomy and Ecology summarized a series of research works, suggesting that the physical environment should be regarded as an important structuring tool for phytoplankton assemblages38; in particular, the light availability has gathered increasing attention38.
Filamentous cyanobacteria seem to have lower optimum temperatures compared to other cyanobacteria, e.g., Planktothrix agardhii can grow better at 18-25 °C39,41–44, while the preferred temperature range of Microcystis is higher (Microcystis aeruginosa: 24-34 °C45–47; Microcystis wesenbergii: 25-35 °C47; Microcystis ichthyoblabe: 30-36 °C48), as summarized by49. Culture studies have shown that temperature is an important factor governing the growth of filamentous cyanobacteria when temperature varies greatly (e.g. > 5 °C, Table 1). For example, the red-pigmented Planktothrix rubescens has more competitive success at 15 °C, while the green-pigmented Planktothrix agardhii is more competitive at 25 °C42. No significant difference of water temperature was observed during July to September from 2013 to 2015 in QCS Reservoir, except the temperature of August in 2015 higher than in 2013 and 2014 (Fig. 5b), suggesting temperature may not the major contributor regarding the replacement of Pseudanabaena from Planktothrix. Nevertheless, the role of temperature regarding the succession and/or competition still requires more specific study, since it usually correlated with light intensity so that it is hard to distinguish its contribution.
In general, a “light niche” specified by light intensity and spectral composition can promote phytoplankton species replacement. Growth rate responses to these different light levels are among major traits that determine the ecological success of phytoplankton species50,51. In this study, our results as demonstrated in Fig. 4 indicated that these two genera still have slight light niche differentiation, which probably is responsible for the replacement of Planktothrix by Pseudanabaena in later years. The light niche differentiation was also verified by the culture experiment (Fig. 5a34), showing that the optimum light dose of Planktothrix is lower than for Pseudanabaena, although these two genera were both recognized as low-irradiance specialists26,27. Furthermore, we summarized the light preferences of 10 Planktothrix strains and 2 Pseudanabaena strains (Table 1), showing a consistent conclusion with this study that the optimum light intensities of all Pseudanabaena strains are lower than those of Planktothrix. Nevertheless, a more targeted comparison is needed to verify the difference of light optimum between the two genera. The competitive advantage of Planktothrix at high solar radiation conditions was weakened in 2014 and 2015 owing to the lowered solar radiation (July-August: 1.27 ± 0.37 mol m-2 d-1 and 1.34 ± 0.41 mol m-2 d-1) compared that in 2013 (1.84 ± 0.33 mol m-2 d-1). On the other hand, Pseudanabaena was promoted in lower light conditions and hence outcompeted Planktothrix in QCS Reservoir. Therefore, the subtle light niche differentiation of these two filamentous genera probably is the driving factor responsible for the succession and/or competition between Planktothrix and Pseudanabaena.
| No. | Strain | Light (μE m-2s^-1) | Temperature (°C) | Ref. |
|---|---|---|---|---|
| 1 | Oscillatoria redekei | 50-180>40>20>10 | 23>17>11>5 | 41 |
| 2 | Oscillatoria agardhii | 50>24≈95>12 | 25>20≈15>30 | 39 |
| 3 | Oscillatoria agardhii | 50≈24≈12>95 | 25≈30≈20>15 | 39 |
| 4 | Planktothrix agardhii | 180>100>50>40>20>10 | 23>17>11>5 | 41 |
| 5 | Planktothrix agardhii | 45-80>110>40>20-25>8 | 52 | |
| 6 | Planktothrix agardhii | 60>80>20≈125>10>5>2 | 25>15 | 42 |
| 7 | Planktothrix agardhii | 60>100≈40>10>500 | 53 | |
| 8 | Planktothrix agardhii | 18>30 | 43 | |
| 9 | Planktothrix rubescens | 300>200>80>60>2-40 | 25>15 | 42 |
| 10 | Planktothrix rubescens | 60-120>35>25>10-15>5 | 20>10 | 44 |
| 11 | Planktothrix sp. | 85>36>250≈17>5 | 34 | |
| 12 | Phormidium tenue | 27>30≈21>24>18 | 54 | |
| 13 | Pseudanabaena sp. | 36>85≈250>17>5 | - | |
| 14 | Pseudanabaena sp. | 10>25>35 | 26 | |
| 15 | Pseudanabaena galeata | 30≈50>100>300≈10>600 | 55 | |
| 16 | Lyngbya kuetzingii | 20>75≈10>0 | 25>35>10 | 56 |
- Oscillatoria agardhii was renamed to Planktothrix agardhii.57
- ≈ denotes approximate growth under the two comparing conditions.
Other factors may also play important roles on their succession, e.g., Planktothrix posses gas vesicles, while Pseudanabaena not. The reservoir is well mixed during the whole year, and the depth of euphotic layer is relatively low due to high turbidity loading from Yangtze River. The gas vesicles of Planktothrix can provide buoyancy that enable the cells to perform vertical migrations or to maintain themselves in the euphotic zone58.
Vellend’s new conceptual synthesis in community ecology59 has identified four distinct processes including selection, drift, speciation and dispersal. Under this framework, the succession of Planktothrix and Pseudanabaena in QCS Reservoir may dominate by the selection process, on the premise of the dispersal process that imported new Pseudanabaena from the Yangtze River, although the supporting evidence for this is limited. Owing to the decrease of irradiance of July and August in 2014 and 2015, we speculate that Pseudanabaena had higher fitness than Planktothrix and that this promoted the replacement.
Solar radiation seems an essential factor governing the competition between the filamentous cyanobacteria Planktothrix and Pseudanabaena in the present study, hence adjusting the underwater light climate could be a possible measure to regulate the filamentous cyanobacteria composition. Besides, since MIB is mainly produced by filamentous cyanobacteria, it is therefore possible to inhibit MIB-producing strains but enhance the non MIB-producing strains by adjusting the light climate. For instance, the non MIB-producing Pseudanabaena is a benign replacement for MIB-producing Planktothrix, by reducing the underwater light intensity by adjusting the water level60 or increasing the turbidity via flow management. The strategy to control odor problems in QCS Reservoir is beyond of this study and will be discussed further in a subsequent publication.
Methods
Study area and laboratory analysis
This is a follow-up study for7. QCS Reservoir (32o27’N, 121o38’E), located in Changxing Island in the Yangtze estuary, is a newly built reservoir used as the drinking water resource for Shanghai. The bathymetry map shows that the water depth in the reservoir varies from 2.7 m in the upstream area to 12.1 m in the downstream area, and an island in the upper section splits the water flow into two branches (Fig. 1a). The hydraulic retention time is in the range of 21.3 ± 2.2 d (April) and 124.1 ± 8.9 (December), the mean water turbidity is in the range of 22.7 ± 23.8 NTU (March) and 40 ± 44.5 NTU (December), and the mean water transparency is in the range of 55 ± 16 cm (September) and 80.35 ± 36.71 cm (March). The reservoir imports the water from Yangtze River via inlet gate. Since the abundance of phytoplankton and the concentrations of odor compounds of the river water are very low, 3 routine sampling sites including QC01 (water intake), QC02 and QC09 (reservoir center) located in the lower section of the reservoir were selected, as illustrated in Fig. 1a. Since the whole reservoir is well-mixed7 throughout the year, 2 L surface water samples (0.5 m) at each site were collected using a Kemmerer water sampler for other water quality.
Phytoplankton analysis was performed weekly from 2011 to 2015; odorant identification and quantification were performed every day from 2011 to 2015; nutrients and other water quality were recorded every day since 2013. Physicochemical variables such as water temperature, pH, dissolved oxygen (DO), turbidity, and conductivity were measured in-situ with a multi-parameter probe (YSI EXO2, Yellow Springs, Ohio, USA).
Subsamples for MIB and geosmin detection were added 10 mg L-1 HgCl2 to prevent biodegradation and stored in light-blocking bottles, and analyzed within 72 h using the solid phase micro-extraction (SPME) method coupled with gas chromatography-mass spectrometry (GC-MS) (Agilent 6890/5975, Agilent Tech., USA)11. SPME was performed using an automated device (Combi PAL GC MultiFunction Autosampler, CTC Analytics, Switzerland) as follows: samples were shaken at 65 °C for 20 min, then the SPME fiber was exposed in the head-space of the vial for 10 min in order to absorb the odor compounds. The fiber was transferred to the injection port of the gas chromatograph and desorbed in the splitless mode at 250 °C for 3 min. Calibration standards for MIB (Supelco Inc.) were used. 2-isopropyl-3-methoxypyrazine (Supelco Inc.) was added to each sample as internal standard. This method has a detection limit of 1 ng L-1 for both compounds.
Subsamples (1000 mL) for cell enumeration were preserved with 5% Lugol’s iodine61 and left to settle for 48 hours, then pre-concentrated 20× and kept in the dark until cell counting. The identification of cyanobacterial species was carried out following62 and revised according to Ref.63. The phytoplankton were identified and enumerated using an upright microscope (Olympus BX53, Japan) following the protocol established by64. The filamentous cyanobacteria abundances were quantified based on the length of each filament and the mean cell length of each strain. The number of cells in colony species such as Microcystis sp. was estimated based on colony volume and mean cell number per volume. The mean cell morphological characteristics including cell length, cell volume etc. were determined according to more than 50 filaments/colonies of each strain using a in-house developed cell counting tool (CCT v1.4, https://drwater.rcees.ac.cn, in Chinese).
The total global radiation (\(I_g\), MJ m-2 d-1) of Chongming Island (< 20 km) was extracted from China Meteorological Data Service Center (CMDC)65. Photosynthetically Active Radiation (\(PAR_E\), 400 - 700 nm, MJ m-2 d-1) values were determined by a simplified model (Eq. 1), according to 30 years of estimations of total global radiation and photosynthetically active radiation (PAR) in central China66.
\[ PAR_E = \frac{1666.4}{3983.9}\times I_{g} = 0.4183I_{g} \tag{1}\]
The PAR quantum (\(PAR_Q\), mol m-2·d-1) was estimated according to Eq. 2, where the coefficient 4.57 (μmol m-2·s-1 per W·m-2) is adopted for the PAR of sky sunlight67.
\[ PAR_{Q} = \frac{PAR_E \times 10^6}{24\times60\times60}\times\frac{1}{4.57}\times\frac{24\times60\times60}{10^6} = \frac{0.4183I_g}{4.57} = 0.09153I_g \tag{2}\]
Culture experiments of filamentous cyanobacteria under different light intensities
Two filamentous cyanobacteria, Planktothrix sp. (FACHB-1375) and Pseudanabaena sp. (FACHB-1277), were obtained from the Freshwater Algae Culture Collection at the Institute of Hydrobiology, FACHB, China. Culture experiments were performed by growing the two genera in BG11 medium68 at 25°C and 5 different light intensities (5, 17, 36, 85, 250 \(\mu\)mol m-2 s-1, 12:12 h light:dark cycle) in accordance with measured light intensities at different depths in the field. The triplicate samples were destructively collected from each set every 4 days over a 35 days’ culture period. Data for Planktothrix has been published in34. The light doses (\(I_{dose}\), mol m-2 d-1) were calculated from the instantaneous light intensities (\(I\), \(\mu\)mol m-2 s-1) and daily radiation time (12 h) according to Eq. 3.
\[ I_{dose}=\frac{12\times60\times60}{10^6}I = 0.0432I \tag{3}\] The growth yield of Planktothrix and Pseudanabaena were determined according to the quantiles (25%, 50%, 75%) of cell densities observed within stationary phase (day 15 to day 35).
Time series analysis
Generalized additive models (GAMs)69 were used to model the seasonal and long-term patterns of environmental factors and filamentous cyanobacteria cell densities, as shown in Eq. 4. Thin plate spline (TS-spline)70 were used to represent the long-term trend terms; while cyclic cubic splines, which have an additional constraint ensuring continuity between the beginning and the end of a year71, were used for the seasonal terms.
\[ y = \beta_0 + f_{\mathrm{seasonal}}(x_1) + f_{\mathrm{trend}}(x_2) + \varepsilon, \quad \varepsilon \sim N(0, \sigma^2) \tag{4}\]
where \(f_{\mathrm{seasonal}}\) and \(f_{\mathrm{trend}}\) are smooth functions for the seasonal and interannual trend of environmental factors and cell densities; \(x_1\) denotes the sampling week number and \(x_2\) denotes the sampling date in units of decimal years. To make it clear, R Language demonstration code is given in the supplementary material.
To avoid autocorrelation from observations of successive time series, which might result in negatively biased estimation of regression coefficients and residuals, a first-order autoregressive model (AR(1), Eq. 5) was employed for the error term.
\[\varepsilon_i = \phi\varepsilon_{i-1} + v_i \tag{5}\]
Different model structures were compared with likelihood ratio tests and the Akaike Information Criterion (AIC).
Ecological niche modelling
Ecological niche modelling (ENM), also known as species distribution modelling (SDM) uses computer algorithms to predict the distribution of a species across geographic space and time using environmental data. Water temperature (\(T\)), pre-week PAR (\(I\), the mean PAR of the week before the sampling date) and nutrients (ammonia nitrogen, total nitrate, total phosphate) were used as predictors (\(x_i\)) of filamentous cyanobacterial abundances (\(y\)).
Here, we use the Generalized Additive Model (GAM) to model the abundances of the two filamentous cyanobacteria, as shown in Eq. 6. We use cell density to utilize more of the available information while many other studies use binary absent/present data to represent the biotic response to environmental conditions.
\[ \log_{10}(1 + (E(y_j)) = \beta_0 + \sum_{j=1}^J \sum_{k=1}^K \delta_{jk}b_{1j}(x_1)b_{2k}(x_2) + \varepsilon_j \tag{6}\]
where \(b_1\) and \(b_2\) are basis functions, \(J\) and \(K\) are corresponding basis dimensions and \(\delta\) is a matrix of unknown coefficients. Interactions among the indicators were evaluated by the tensor product (\(f_1(x_1)\otimes f_2(x_2)\)72).
ENM were performed following several steps as describe below:
- Correlation analysis between filamentous cyanobacteria abundances (including Planktothrix and Pseudanabaena) and environmental factors as the potential predictors according to the two linear models (name as LM1 and LM2) as summarized in Table S9 and Table S10;
- Backward Stepwise model simplification of LM1 and LM2 were performed to further sort out the possible predictors for both genus (Table S11 and Table S12);
- Variance inflation factors (VIF) were computed for the predictors given by step 2 (Table S13);
- Correlation coefficients among the predictors were calculated using Pearson method to evaluate the potential interacting effects among predictors (Fig. S7);
- Based on the results of step 4, the predictors were assembled accordingly with appropriate smooth functions of GAM models for Planktothrix (named as GAM1, Table S14) and Pseudanabaena (named as GAM2, Table S15);
- ENMs (named as GAM3 and GAM4) were optimized according to the importance of predictors in GAM1 and GAM2 (Table S16 and Table S17).
Estimated abundances of targeted species versus environmental factors were illustrated with contour maps. Quantile niche space was identified by the boundary defined by the 90% quantile of estimated abundances for each genus.
Statistical analysis and illustration
All data analysis and illustration were performed using R 4.073. Data pretreatment and summary were performed using the dplyr74 package in R, regression analysis including linear and generalized linear models were performed using the stats package73, generalized additive modelling was performed using the mgcv package75,76 quantile regression analysis was performed using the quantreg package77; contour figures were created by the graphics package73, other figures were prepared using the ggplot2 package78.
Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request and with the permission from Shanghai Chengtou Raw Water Co. Ltd.. The code used to develop individual figures is available upon request to the corresponding author.
Acknowledgement
This work was financially supported by the National Key R&D Program of China (2018YFE0204101), the National Natural Science Foundation of China (51878649, 52030002), and Youth Innovation Promotion Association CAS.
Competing interests
The authors declare no competing interests.
References
1.
AWWA, A. W. W. A. Algae: Source to treatment. (American Water Works Association, 2010).
2.
Berglind, L., Holtan, H. & Skulberg, O. M. Case studies on off-flavours in some norwegian lakes. Water Science and Technology 15, 199–209 (1983).
3.
Izaguirre, G., Jungblut, A. & Neilan, B. A. Benthic cyanobacteria (oscillatoriaceae) that produce microcystin-LR, isolated from four reservoirs in Southern California. Water Research 41, 492–498 (2007).
4.
Sun, D. et al. Occurrence of odor problems in drinking water of major cities across China. Frontiers of Environmental Science & Engineering 8, 411–416 (2014).
5.
Suurnäkki, S. et al. Identification of geosmin and 2-methylisoborneol in cyanobacteria and molecular detection methods for the producers of these compounds. Water Research 68, 56–66 (2015).
6.
Tsuchiya, Y. & Matsumoto, A. Identification of volatile metabolites produced by blue-green algae. Water Science and Technology 20, 149 (1988).
7.
Su, M. et al. Identification of MIB producers and odor risk assessment using routine data: A case study of an estuary drinking water reservoir. Water Research 192, 116848 (2021).
8.
Izaguirre, G., Hwang, C. J., Krasner, S. W. & Mcguire, M. J. Geosmin and 2-methylisoborneol from cyanobacteria in three water supply systems. Applied and Environmental Microbiology 43, 708–714 (1982).
9.
Wu, J. & Jüttner, F. Differential partitioning of geosmin and 2-methylisoborneol between cellular constituents in Oscillatoria tenuis. Archives of Microbiology 150, 580–583 (1988).
10.
Martin, J. F., Izaguirre, G. & Waterstrat, P. A planktonic Oscillatoria species from Mississippi Catfish Ponds that produces the off-flavor compound 2-methylisoborneol. Water Research 25, 1447–1451 (1991).
11.
Su, M. et al. MIB-producing cyanobacteria (Planktothrix sp.) in a drinking water reservoir: Distribution and odor producing potential. Water Research 68, 444–453 (2015).
12.
Izaguirre, G. & Taylor, W. D. A Pseudanabaena species from Castaic Lake, California, that produces 2-methylisoborneol. Water Research 32, 1673–1677 (1998).
13.
Tabachek, J. L. & Yurkowski, M. Isolation and identification of blue-green algae producing muddy odor metabolites, geosmin, and 2-methylisoborneol, in saline lakes in Manitoba. Journal of the Fisheries Research Board of Canada 33, 25–35 (1976).
14.
Lu, J. et al. Driving forces for the growth of MIB-producing Planktothricoides raciborskii in a low-latitude reservoir. Water Research 118670 (2022) doi:10.1016/j.watres.2022.118670.
15.
Li, Z. et al. Earthy odor compounds production and loss in three cyanobacterial cultures. Water Research 46, 5165–5173 (2012).
16.
Sun, D. et al. Identification of causative compounds and microorganisms for musty odor occurrence in the Huangpu River, China. Journal of Environmental Sciences 25, 460–465 (2013).
17.
Chiu, Y., Yen, H. & Lin, T. An alternative method to quantify 2-MIB producing cyanobacteria in drinking water reservoirs: Method development and field applications. Environmental Research 151, 618–627 (2016).
18.
Wang, Z. et al. Establishment and field applications of Real-time PCR methods for the quantification of potential MIB-producing cyanobacteria in aquatic systems. Journal of Applied Phycology 28, 325–333 (2015).
19.
Filstrup, C. T., Heathcote, A. J., Kendall, D. L. & Downing, J. A. Phytoplankton taxonomic compositional shifts across nutrient and light gradients in temperate lakes. Inland Waters 6, 234–249 (2016).
20.
Wang, H. et al. Light, but not nutrients, drives seasonal congruence of taxonomic and functional diversity of phytoplankton in a eutrophic highland lake in China. Frontiers in Plant Science 11, 179 (2020).
21.
Polimene, L. et al. Modelling a light-driven phytoplankton succession. Journal of Plankton Research 36, 214–229 (2013).
22.
Su, M., An, W., Yu, J., Pan, S. & Yang, M. Importance of underwater light field in selecting phytoplankton morphology in a eutrophic reservoir. Hydrobiologia 724, 203–216 (2014).
23.
Holtrop, T. et al. Vibrational modes of water predict spectral niches for photosynthesis in lakes and oceans. Nature Ecology & Evolution 5, 55–66 (2021).
24.
Naselli-flores, L., Padisák, J. & Albay, M. Shape and size in phytoplankton ecology: Do they matter? Hydrobiologia 578, 157–161 (2007).
25.
Su, M. et al. Succession and interaction of surface and subsurface cyanobacterial blooms in oligotrophic/mesotrophic reservoirs: A case study in Miyun Reservoir. Science of the Total Environment 649, 1553–1562 (2019).
26.
Wang, Z. & Li, R. Effects of light and temperature on the odor production of 2-methylisoborneol-producing Pseudanabaena sp. and geosmin-producing Anabaena ucrainica (cyanobacteria). Biochemical Systematics and Ecology 58, 219–226 (2015).
27.
Halstvedt, C. B., Rohrlack, T., Andersen, T., Skulberg, O. & Edvardsen, B. Seasonal dynamics and depth distribution of Planktothrix spp. In Lake Steinsfjorden (Norway) related to environmental factors. Journal of Plankton Research 29, 471–482 (2007).
28.
Tilman, D. Resource competition and community structure. (Princeton university press, 1982).
29.
Tilman, D. Niche tradeoffs, neutrality, and community structure: A stochastic theory of resource competition, invasion, and community assembly. Proceedings of the National Academy of Sciences of the United States of America 101, 10854 (2004).
30.
Warren, D. L. & Seifert, S. N. Ecological niche modeling in maxent: The importance of model complexity and the performance of model selection criteria. Ecological Applications 21, 335–342 (2011).
31.
Huisman, J. et al. Cyanobacterial blooms. Nature Reviews Microbiology 16, 471–483 (2018).
32.
Paerl, H. W. & Huisman, J. Blooms like it hot. Science 320, 57–58 (2008).
33.
Paerl, H. W., Fulton, R. S., Moisander, P. H. & Dyble, J. Harmful freshwater algal blooms, with an emphasis on cyanobacteria. The Scientific World Journal 1, 76–113 (2001).
34.
Jia, Z. et al. Light as a possible regulator of MIB-producing Planktothrix in source water reservoir, mechanism and in-situ verification. Harmful Algae 88, 101658 (2019).
35.
Su, M. et al. Ecological niche and in-situ control of MIB producers in source water. Journal of Environmental Sciences 110, 119–128 (2021).
36.
Scheffer, M., Rinaldi, S., Gragnani, A., Mur, L. R. & Van Nes, E. H. On the dominance of filamentous cyanobacteria in shallow, turbid lakes. Ecology 78, 272–282 (1997).
37.
Sugiura, N., Iwami, N., Inamori, Y., Nishimura, O. & Sudo, R. Significance of attached cyanobacteria relevant to the occurrence of musty odor in Lake Kasumigaura. Water Research 32, 3549–3554 (1998).
38.
Zohary, T., Padisák, J. & Naselli-flores, L. Phytoplankton in the physical environment: Beyond nutrients, at the end, there is some light. Hydrobiologia 639, 261–269 (2010).
39.
Sivonen, K. Effects of light, temperature, nitrate, orthophosphate, and bacteria on growth of and hepatotoxin production by Oscillatoria agardhii strains. Applied and Environmental Microbiology 56, 2658–2666 (1990).
40.
Heath, M., Wood, S. A., Young, R. G. & Ryan, K. G. The role of nitrogen and phosphorus in regulating Phormidium sp. (Cyanobacteria) growth and anatoxin production. FEMS Microbiology Ecology 92, fiw021 (2016).
41.
Foy, R. H. Interaction of temperature and light on the growth rates of two planktonic Oscillatoria species under a short photoperiod regime. British Phycological Journal 18, 267–273 (1983).
42.
Oberhaus, L., Briand, J. F., Leboulanger, C., Jacquet, S. & Humbert, J. F. Comparative effects of the quality and quantity of light and temperature on the growth of Planktothrix agardhii and P. rubescens. Journal of Phycology 43, 1191–1199 (2007).
43.
Anunciação Gomes, A. M. da, Oliveira e Azevedo, S. M. F. de & Lürling, M. Temperature effect on exploitation and interference competition among Microcystis aeruginosa, Planktothrix agardhii and, Cyclotella meneghiniana. The Scientific World Journal 2015, 1–10 (2015).
44.
Bright, D. I. & Walsby, A. E. The daily integral of growth by Planktothrix rubescens calculated from growth rate in culture and irradiance in Lake Zürich. New Phytologist 146, 301–316 (2000).
45.
Ganf, G. G. Rates of oxygen uptake by the planktonic community of a shallow equatorial lake (Lake George, Uganda). Oecologia 15, 17–32 (1974).
46.
You, J., Mallery, K., Hong, J. & Hondzo, M. Temperature effects on growth and buoyancy of Microcystis aeruginosa. Journal of Plankton Research 40, 16–28 (2017).
47.
Imai, H., Chang, K., Kusaba, M. & Nakano, S. Temperature-dependent dominance of Microcystis (cyanophyceae) species: M. aeruginosa and M. wesenbergii. Journal of Plankton Research 31, 171–178 (2009).
48.
Mowe, M. A. D. et al. Rising temperatures may increase growth rates and microcystin production in tropical Microcystis species. Harmful Algae 50, 88–98 (2015).
49.
Li, J., Amano, Y. & Machida, M. Temperature-dependent growth characteristics and dominance trends of the cyanobacterium Microcystis sp. And the diatom Cyclotella meneghiniana. Hydrobiologia 849, 1677–1688 (2022).
50.
Litchman, E. & Klausmeier, C. A. Trait-based community ecology of phytoplankton. Annual Review of Ecology, Evolution, and Systematics 39, 615–639 (2008).
51.
Tezanos Pinto, P. & Litchman, E. Eco-physiological responses of nitrogen-fixing cyanobacteria to light. Hydrobiologia 639, 63–68 (2010).
52.
Tonk, L. et al. The microcystin composition of the cyanobacterium Planktothrix agardhii changes toward a more toxic variant with increasing light intensity. Applied and Environmental Microbiology 71, 5177–5181 (2005).
53.
Araujo Torres, C. de, Lürling, M. & Marinho, M. M. Assessment of the effects of light availability on growth and competition between strains of Planktothrix agardhii and Microcystis aeruginosa. Microbial Ecology 71, 802–813 (2015).
54.
Fujimoto, N., Sudo, R., Sugiura, N. & Inamori, Y. Nutrient-limited growth of Microcystis aeruginosa and Phormidium Tenue and competition under various N:P supply ratios and temperatures. Limnology and Oceanography 42, 250–256 (1997).
55.
56.
Zhang, T., Li, L., Song, L. & Chen, W. Effects of temperature and light on the growth and geosmin production of Lyngbya kuetzingii (cyanophyta). Journal of Applied Phycology 21, 279–285 (2008).
57.
Anagnostidis, K. & Komárek, J. Modern approach to the classification system of cyanophytes. 3 - oscillatoriales. Algological Studies/Archiv Für Hydrobiologie, Supplement Volumes 50-53, 327–472 (1988).
58.
Porat, R., Teltsch, B., Mosse, R. A., Dubinsky, Z. & Walsby, A. E. Turbidity changes caused by collapse of cyanobacterial gas vesicles in water pumped from Lake Kinneret into the Israeli national water carrier. Water Research 33, 1634–1644 (1999).
59.
Vellend, M. Conceptual synthesis in community ecology. The Quarterly Review of Biology 85, 183–206 (2010).
60.
Su, M. et al. Reducing production of taste and odor by deep-living cyanobacteria in drinking water reservoirs by regulation of water level. Science of the Total Environment 574, 1477–1483 (2017).
61.
Sherr, E. B. & Sherr, B. F. Preservation and storage of samples for enumeration of heterotrophic protists. Handbook of Methods in Aquatic Microbial Ecology. Lewis Publishers, Boca Raton 207–212 (1993).
62.
Komárek, J. & Anagnostidis, K. Cyanoprokaryota 1. Susswasserflora Von Mitteleuropa vol. 19(1) (Deu, 1998).
63.
Komarek, J., Kastovsky, J., Mares, J. & Johansen, J. R. Taxonomic classification of cyanoprokaryotes (cyanobacterial genera) 2014, using a polyphasic approach. Preslia 86, 295–335 (2014).
64.
Martinez, M., Chakroff, R. & Pantastico, J. Note: Direct phytoplankton counting techniques, using the haemacytometer. Philippine agriculturist (1975).
65.
CMDSC, C. M. D. S. C. Data set of daily radiation in China. (2018).
66.
Ren, X., He, H., Zhang, L. & Yu, G. Global radiation, photosynthetically active radiation, and the diffuse component dataset of China, 1981–2010. Earth System Science Data 10, 1217–1226 (2018).
67.
Thimijan, R. W. & Heins, R. D. Photometric, radiometric, and quantum light units of measure: A review of procedures for interconversion. Hortscience 18, 818–822 (1983).
68.
Castenholz, R. W. Culturing methods for cyanobacteria. in Cyanobacteria vol. 167 68–93 (Academic Press, 1988).
69.
Hastie, T. J. & Tibshirani, R. J. Generalized additive models. vol. 43 (Crc Press, 1990).
70.
Wood, S. N. Thin-plate regression splines. Journal of the Royal Statistical Society (b) 65, 95–114 (2003).
71.
Adams, J. A. Cubic spline curve fitting with controlled end conditions. Computer-Aided Design 6, 2–9 (1974).
72.
Wood, S. N., Scheipl, F. & Faraway, J. J. Straightforward intermediate rank tensor product smoothing in mixed models. Statistics and Computing 23, 341–360 (2013).
73.
R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2020).
74.
Wickham, H., François, R., Henry, L. & Müller, K. dplyr: A grammar of data manipulation. (2018).
75.
Wood, S. N. Stable and efficient multiple smoothing parameter estimation for generalized additive models. Journal of the American Statistical Association 99, 673–686 (2004).
76.
Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society (b) 73, 3–36 (2011).
77.
Koenker, R. Quantreg: Quantile regression. (2017).
78.
Wickham, H. ggplot2: Elegant graphics for data analysis. (Springer-verlag New York, 2016).