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−2 d−1) are lower than those of _Planktothrix_ (3.67 mol m−2 d−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. SIMILAR CONTENT BEING VIEWED BY OTHERS MULTIPLE ROLES OF BAMBOO AS A REGULATOR OF CYANOBACTERIAL BLOOM IN AQUATIC SYSTEMS Article Open access 31

January 2022 DYNAMIC BACTERIAL COMMUNITY RESPONSE TO _AKASHIWO SANGUINEA_ (DINOPHYCEAE) BLOOM IN INDOOR MARINE MICROCOSMS Article Open access 26 March 2021 COMPETITIVE INTERACTIONS AS A

MECHANISM FOR CHEMICAL DIVERSITY MAINTENANCE IN _NODULARIA SPUMIGENA_ Article Open access 26 April 2021 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 southward2,3,4,5,6,7. The major

MIB producers include _Oscillatoria_8,9,10, _Planktothrix_11, _Phormidium_10, _Pseudanabaena_12, _Lyngbya_13 and _Planktothricoides_14. It should be noted that MIB yield varies among

different strains11,15,16,17, 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 models21,22,23. 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 modeling25,28,29,30. 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 _Microcystis_7, 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

(Supplementary Fig. 1). 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 2 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 (Supplementary Fig. 2); _Planktothrix_ (30.2%) and _Pseudanabaena_ (30.5%) exhibited higher occurrence frequencies than

_Phormidium_ (14.9%) and _Lyngbya_ (2.5%). _Lyngbya_ was only observed for eight 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 (Supplementary Fig. 3). _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 (Supplementary Fig. 3). Therefore, _Pseudanabaena_ was

considered as the most important competitor to _Planktothrix_ based on their seasonal distribution patterns (Supplementary Fig. 3) 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), Supplementary Table 7, Supplementary Fig. 5). 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 (Supplementary Table 8, Supplementary Fig. 6), 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 Figure 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 (Supplementary Tables 1–4), 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 (Supplementary Tables 5 and 6). 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 MODELING OF _PLANKTOTHRIX_ AND _PSEUDANABAENA_ According to several published research31,32,33 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_ (\(Y_1 = \log _{10}\left( {N_1 + 1} \right)\), Supplementary Table 9) and _Pseudanabaena_ (\(Y_2 =

\log _{10}\left( {N_2 + 1} \right)\), Supplementary Table 10) associated with backward stepwise selection (Supplementary Tables 11 and 12) and variance inflation factor (VIF, Supplementary

Table 13). These predictors were subsequently classified into 4 groups based on the correlation analysis between each two predictors (Supplementary Fig. 7), 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 modeling the interaction between T and I by bivariate tensor-product smoothers. GAM models for abundances of _Planktothrix_ (GAM1, Supplementary Table 14) and _Pseudanabaena_

(GAM2, Supplementary Table 15) 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, Supplementary Table 16) and _Pseudanabaena_

(GAM4, Supplementary Table 17) 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 c. _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 15–35) increased along with the light dose when it was <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 >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 2 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 (Figs. 1 and 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 2 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 agardhii_39 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

 °C39,41,42,43,44, while the preferred temperature range of _Microcystis_ is higher (_Microcystis aeruginosa_: 24–34 °C45,46,47; _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, Supplementary 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_. 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 zone52. Vellend’s new conceptual synthesis in

community ecology53 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 level54 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 (32°27'N, 121°38'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, three 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 iodine55 and left to settle for 48 h, then pre-concentrated 20× and kept in the dark until cell counting. The identification of cyanobacterial

species was carried out following56 and revised according to ref. 57. The phytoplankton were identified and enumerated using an upright microscope (Olympus BX53, Japan) following the

protocol established by58. 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 >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)59. 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 China60. $$PAR_E = \frac{{1666.4}}{{3983.9}} \times I_g = 0.4183I_g$$ (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 sunlight61. $$PAR_Q = \frac{{PAR_E \times 10^6}}{{24 \times 60 \times 60}} \times \frac{1}{{4.57}} \times \frac{{24 \times 60 \times

60}}{{10^6}} = \frac{{0.4183I_g}}{{4.57}} = 0.09153I_g$$ (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 medium62 at 25 °C and 5 different light intensities (5, 17, 36, 85, 250 _μ_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_, _μ_mol m−2 s−1) and daily radiation time (12 h) according to Eq.

(3). $$I_{dose} = \frac{{12 \times 60 \times 60}}{{10^6}}I = 0.0432I$$ (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 15–35). TIME SERIES ANALYSIS Generalized additive models (GAMs)63 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)64 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 year65, were used for the seasonal terms. $$y = \beta _0 +

f_{{{{\mathrm{seasonal}}}}}\left( {x_1} \right) + f_{{{{\mathrm{trend}}}}}\left( {x_2} \right) + \varepsilon ,\quad \varepsilon \sim N\left( {0,\sigma ^2} \right)$$ (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. $$\varepsilon _i = \phi \varepsilon _{i - 1} + v_i$$ (5) Different model structures were compared with likelihood ratio tests and the Akaike

Information Criterion (AIC). ECOLOGICAL NICHE MODELING Ecological niche modeling (ENM), also known as species distribution modeling (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. $${{{\mathrm{log}}}}_{10}\left( {1 + \left( {E\left( {y_j} \right)} \right)} \right. = \beta _0 + \mathop

{\sum}\limits_{j = 1}^J {\mathop {\sum}\limits_{k = 1}^K {\delta _{jk}b_{1j}\left( {x_1} \right)b_{2k}\left( {x_2} \right) + \varepsilon _j} }$$ (6) where _b_1 and _b_2 are basis functions,

_J_ and _K_ are corresponding basis dimensions and _δ_ is a matrix of unknown coefficients. Interactions among the indicators were evaluated by the tensor product (\(f_1\left( {x_1} \right)

\otimes f_2\left( {x_2} \right)\)66). ENM were performed following several steps as describe below: * 1. 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 Supplementary Tables 9 and

10; * 2. Backward Stepwise model simplification of LM1 and LM2 were performed to further sort out the possible predictors for both genus (Supplementary Tables 11 and 12); * 3. Variance

inflation factors (VIF) were computed for the predictors given by step 2 (Supplementary Table 13); * 4. Correlation coefficients among the predictors were calculated using Pearson method to

evaluate the potential interacting effects among predictors (Supplementary Fig. 6); * 5. Based on the results of step 4, the predictors were assembled accordingly with appropriate smooth

functions of GAM models for _Planktothrix_ (named as GAM1, Supplementary Table 14) and _Pseudanabaena_ (named as GAM2, Supplementary Table 15); * 6. ENMs (named as GAM3 and GAM4) were

optimized according to the importance of predictors in GAM1 and GAM2 (Supplementary Tables 16 and 17). 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 4.067. Data pretreatment and summary were performed using the DPLYR68 package in R, regression analysis including linear and generalized

linear models were performed using the STATS package67, generalized additive modeling was performed using the MGCV package69,70 quantile regression analysis was performed using the QUANTREG

package71; contour figures were created by the GRAPHICS package67, other figures were prepared using the GGPLOT2 package72. DATA AVAILABILITY The data that support the findings of this study

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Key R&D Program of China (2018YFE0204101), the National Natural Science Foundation of China (51878649, 52030002), and Youth Innovation Promotion Association CAS. AUTHOR INFORMATION

AUTHORS AND AFFILIATIONS * Key Laboratory of Drinking Water Science and Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China Ming

Su, Zhiyong Yu, Jinping Lu, Tengxin Cao, Jianwei Yu, Yu Zhang & Min Yang * University of Chinese Academy of Sciences, Beijing, 100049, China Ming Su, Zhiyong Yu, Jinping Lu, Tengxin Cao,

 Jianwei Yu, Yu Zhang & Min Yang * Shanghai Chengtou Raw Water Co. Ltd, Shanghai, 200125, China Yiping Zhu & Yichao Song * Department of Biosciences, University of Oslo, P.O. Box

1066 Blindern, 0316, Oslo, Norway Tom Andersen * National Engineering Research Center of China (South) for Urban Water, Shanghai, 200082, China Xianyun Wang Authors * Ming Su View author

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Google Scholar * Min Yang View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS M.S.: Funding acquisition, data analysis, writing, reviewing and

editing. Y.Z.: Data collation, laboratory testing. T.A.: Method guidance, reviewing and editing. X.W.: Laboratory testing. Z.Y.: Laboratory testing. J.L.: Data analysis. Y.S.: Sample

collection, laboratory testing. J.Y.: Reviewing. Y.Z.: Reviewing. M.Y.: funding acquisition, reviewing and editing. CORRESPONDING AUTHOR Correspondence to Min Yang. ETHICS DECLARATIONS

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