Temporal and vertical variation of phytoplankton and zooplankton in two tropical reservoirs with different trophic states

Abstract

In tropical reservoirs, limnological factors are responsible for changes in plankton and vary at temporal and vertical scales. The aim of this study was to evaluate the effects of temporal and vertical variation of the water column on phytoplankton and zooplankton dynamics in two tropical reservoirs (mesotrophic and supereutrophic) in Northeastern Brazil. Monthly collections from three depths in the limnetic region of the reservoirs were performed to analyze the phytoplankton, zooplankton, and limnological variables. The temporal and vertical variation of the physical and chemical water variables, including their interactions, influenced the phytoplankton and zooplankton community. In the supereutrophic reservoir, decreased nitrogen and increased phosphorus and temperature contributed to the dominance of Microcystis panniformis Komárek, Komárková-Legnerová, Sant’Anna, M.T.P.Azevedo & P.A.C.Senna. Conversely, Planktothrix agardhii (Gomont) Anagnostidis & Komárek was dominant under high nitrogen concentrations and low temperatures. In the mesotrophic reservoir, the desmids were dominant and showed a positive relationship with nitrogen. Copepoda Calanoida was dominant and correlated to phytoplankton in both reservoirs. The results showed that nitrogen forms directly influenced phytoplankton, and the importance of nitrogen for management strategies of tropical reservoirs, as well as demonstrated the ability of Calanoida to adapt to different trophic conditions and phytoplankton compositions.

Key words
Calanoida; cyanobacteria; desmids; eutrophication; nitrogen

INTRODUCTION

Climate change and eutrophication have caused strong changes in aquatic ecosystems. With the consequent increase in temperature, reduction of water levels, and nutrient enrichment (Van Zuiden et al. 2016), many shallow reservoirs around the world have gone from a clear state dominated by aquatic macrophytes to a turbid state dominated by cyanobacteria blooms (Dong et al. 2018). Although some studies have been conducted about this topic, the main factors influencing phytoplankton and cyanobacteria dynamics still need to be studied further (Jeppesen et al. 2010, Elliott 2012, Rigosi et al. 2014).

Increased temperature and nutrient concentrations are two factors responsible for increased cyanobacterial blooms (Lürling et al. 2018). Long-term monitoring studies suggest that phosphorus (P) is the main nutrient responsible for cyanobacteria blooms in temperate and tropical regions (Downing et al. 2001, Anneville et al. 2005), however, according to Kosten et al. (2012), high temperatures are also important factors for algae growth. Nutrients play a fundamental role in phytoplankton dynamics in oligotrophic lakes, while temperature is the most important factor in mesotrophic lakes, and synergism between temperature and nutrients influence phytoplankton in eutrophic lakes (Rigosi et al. 2014).

In addition to phosphorus (P), the availability of nitrogen (N) forms in the water favors the growth of cyanobacteria and eukaryotic algae (Chaffin et al. 2013, Davis et al. 2015). Among cyanobacteria, non-diazotrophic filamentous species are favored under increasing nitrogen concentrations (Paerl & Otten 2016); and among eukaryotic algae, desmids are strongly influenced by a variety of nitrogen forms (Mataloni et al. 2015). Generally, most desmids are found in oligotrophic and mesotrophic environments adhered to macrophytes or as part of phytoplankton (Negro et al. 2003). However, some desmids species are adapted to increased nitrogen and phosphorus, such as Staurastrum leptocladum Nordstedt, which are found in eutrophic environments (González & Roldán 2019, Bortolini et al. 2019). Mataloni et al. (2015) verified that planktonic desmids showed a preference for minerotrophic water conditions in swamp pools.

Phytoplankton species show different morphophysiological strategies in response to environmental conditions, such as the presence of gaseous vesicles in cyanobacteria (Harke et al. 2016), or active displacement in the water column by phytoflagellates (Shikata et al. 2015). Along with composition and structure, the vertical distribution of phytoplankton in the water column can be regulated by environmental factors (Rao et al. 2018), such as availability of light and nutrients, mixing zone, dissolved oxygen and wind speed (Cao et al. 2006, Sevindik et al. 2017).

Similarly, the vertical composition and dynamics of zooplankton in the water column respond to changes in phytoplankton composition and water conditions (Hampton et al. 2014, Simoncelli et al. 2019), which has recently aroused the interest of researchers from around the world (Hansson & Hylander 2009, Vadadi-Fülöp et al. 2012). Studies show that temperature and luminosity are the main factors influencing the vertical dynamics of zooplankton (Tiberti & Barbieri 2011, Simoncelli et al. 2019). However, the reason why zooplankton continues to migrate in the water column is multifactorial, what is not fully understood, and still needs to be further assessed, since these organisms are important in trophic networks.

Zooplankton is a fundamental part of the trophic chain, as it is a link between primary producers, i.e. phytoplankton, and secondary consumers such as planktivorous fish (Koel et al. 2019), and acts in energy transfer to higher trophic levels. The presence of planktivorous fish also affects the vertical distribution patterns of zooplankton, because zooplankton tends to migrate vertically in the water column to escape predators (Rhode et al. 2001, Tiberti & Iacobuzio 2013), which majorly impacts the trophic network.

We conducted a monitoring study that analyzed the factors that influence temporal and vertical variation of phytoplankton and zooplankton in two tropical reservoirs with different trophic states. The hypotheses tested were: (i) the composition and biomass of phytoplankton differ temporally and spatially in waters enriched with nitrogen or phosphorus, with the dominance of non-diazotrophic filamentous cyanobacteria under high nitrogen concentrations; (ii) the temporal and vertical dynamics of Copepoda Calanoida are negatively associated with Cyanobacteria and positively with Chlorophyta dominance in phytoplankton.

MATERIALS AND METHODS

Study area

Tapacurá (8°02’31.9”S, 35°11’46.5”W) and Tabocas (8°14’58.3”S, 36°22’42.1”W) reservoirs are located in Pernambuco State, Northeastern, Brazil (Figure 1). The reservoirs are located in the “As” climate region, according to the Köppen climate classification (Alvares et al. 2013). The Tapacurá reservoir is supereutrophic and has a maximum depth of approximately 12 m (Diniz et al. 2019) and maximum accumulation capacity of 94,200,000 m³, with the rainy season occurring from March to August, and dry season from September to February (APAC 2017). This reservoir has a history of perennial blooms of the cyanobacteria Raphidiopsis (previously Cylindrospermopsis) raciborskii (Woloszynska) Aguilera, Berrendero Gómez, Kastovsky, Echenique & Salerno (Aguilera et al. 2018), Microcystis aeruginosa (Kützing) Kützing, Microcystis panniformis Komárek, Komárková-Legnerová, Sant’Anna, M.T.P.Azevedo, & P.A.C.Senna and Planktothrix agardhii (Gomont) Anagnostidis & Komárek (Moura et al. 2018).

Figure 1
Location of the Tapacurá (supereutrophic) and Tabocas (mesotrophic) reservoirs in the municipalities of São Lourenço da Mata and Belo Jardim in the State of Pernambuco, Northeast, Brazil.

Tabocas reservoir is mesotrophic with chlorophytes dominance (Diniz et al. 2019) and has dense banks of submerged macrophyte Egeria densa Planchon (in the present study). Its maximum depth is 4 m (Diniz et al. 2019), maximum accumulation capacity is 1,168,000 m³. The rainy season occurs from March to July, and the dry season occurs from August to February (APAC 2017). These ecosystems are primarily used for public supply and recreational activities.

Sampling and laboratory procedures

Water samples were collected during the rainy (July/2016 and March/2017) and dry (September/2016 and January/2017) seasons from both reservoirs at a single sampling station located in the limnetic region and at three depths using a van Dorn bottle: surface, euphotic zone limit (Z_eu) and bottom. The Z_eu was estimated by multiplying the value of the water transparency (m), determined with the Secchi disc, by factor 2.7 (Esteves 2011) and the maximum depth (Z_max) (called bottom) was determined with a portable ecobatimeter (Hondex PS-7 model). The Z_eu:Z_max ratio was used as the availability of light in the water column (Jensen et al. 1994).

The abiotic variables of water, temperature (ºC), dissolved oxygen (mg L^-1), pH, dissolved total solids (mg L^-1), and electrical conductivity (μS cm^-1) were analyzed in situ with a multiparametric probe (model HANNA HI 9829). Water transparency (m) was measured by the disappearance of the Secchi disc and the luminous intensity (μmol photons m^-2 s^-1) was measured with a photometer (model LI-250A). Precipitation data (mm) was obtained from the National Meteorological Institute database (INMET 2017).

Samples were collected to determine nutrient concentrations from the three depths with a van Dorn bottle and stored in 300 mL plastic containers, transported under refrigeration, and frozen to -4°C in the laboratory until analysis. Total phosphorus (TP; µg L^-1) and orthophosphate (P-PO₄ ^3-; µg L^-1) (Strickland & Parsons 1972), nitrite (N-NO₂ ^-; µg L^-1) and nitrate (N-NO₃ ^-; µg L^-1) (Mackereth et al. 1978), and ammoniacal nitrogen (N-NH₄ ⁺; µg L^-1) (Koroleff 1976) were analyzed. The dissolved inorganic nitrogen (DIN; µg L^-1) concentrations were estimated by the sum of nitrate, nitrite, and ammoniacal nitrogen.

Phytoplankton analysis

Phytoplankton was quantified using samples collected at the three depths using a van Dorn bottle, and fixed with 1% acetic lugol. Taxa were identified using taxonomic bibliographies for each phytoplankton group, such as Prescott & Vinyard (1982), Komárek & Anagnostidis (1999, 2005), Popovský & Pfiester (1990), Krammer & Lange-Bertalot (1991), John et al. (2002) and Komárek (2013), to the lowest taxonomic level possible. The phytoplankton density (ind mL^-1) was determined by counting organisms in sedimentation chambers using an inverted microscope (Zeiss, Axiovert) according to Utermöhl (1958), and the cell volume was calculated from geometric models according to the shape of the cells (Hillebrand et al. 1999). Phytoplankton species biomass was determined by multiplying the density by the mean algal volume of the species, and expressed in mg L^-1, admitting that the volume of 1 mm³ is equivalent to 1 mg of fresh weight of phytoplankton (Wetzel & Likens 2000). The dominance was determined according to (Lobo & Leighton 1986).

Zooplankton analysis

Fifty liters of water were collected from each reservoir at the three depths with a van Dorn bottle and filtered with a 68 μm mesh plankton net to collect zooplankton. The samples were fixed with 4% formaldehyde. The species were identified according to Koste (1978), Montú & Goeden (1986) and Elmoor-Loureiro (1997), to the lowest taxonomic level possible. To quantify the density of organisms (ind L^-1), samples were concentrated and diluted to 100 mL with distilled water and three subsamples (2 mL) were counted in a Sedgwick-Rafter chamber. The biovolume of the taxa was calculated according to the geometric formulas of Ruttner-Kolisko (1977) for rotifers and Dumont et al. (1975) for cladocerans and copepods. Zooplankton biomass (μg PS m^-3) was estimated using density and average biovolume of the taxa. Species dominance was calculated according to Lobo & Leighton (1986).

Statistical analyses

PERMANOVA was performed to verify the differences in abiotic, phytoplankton, and zooplankton variables between reservoirs. ANOVA two-way was performed to verify the differences in abiotic, phytoplankton, and zooplankton variables between depths and months in each reservoir, and verify differences in the biomass of diazotrophic filamentous, non-diazotrophic filamentous, and colonial cyanobacteria between months and depths in each reservoir. The Tukey’s HSD test was performed when significant difference was observed between the variables. Variance analyses were preceded by the Kolmogorov-Smirnov normality test and Bartlett’s homoscedasticity test. The non-parametric Kruskal-Wallis test (test H) was used for non-homoscedastic data.

Principal component analysis (PCA) was performed to evaluate the ordination of environmental abiotic factors (electrical conductivity, luminous intensity, dissolved oxygen, pH, total dissolved solids, water temperature, nitrate, nitrite, ammoniacal nitrogen, DIN, orthophosphate, total phosphorus, Z_eu, and Z_eu:Z_max) between reservoirs, based on a correlation matrix. Redundancy Analyses (RDA) were used to identify the relationships between phytoplankton and zooplankton with abiotic variables aforementioned, assuming that phytoplankton influence zooplankton and vice versa. RDA was applied based on the length of the first axis of the Detrended Correspondence Analysis (DCA). The dependent variables were log-transformed, and the explanatory variables were standardized by the “decostand” function. The explanatory variables were selected by the Forward procedure using the Ordistep function with 999 permutations (p < 0.05), and the collinearity of the variables through the variance of inflation factor (VIF < 20). For phytoplankton, species with biomass above 1% total biomass were considered, for zooplankton the biomasses of all species were considered. All statistical analyses were performed with the vegan package in the R program (R Development Core Team 2015) with a significance of p < 0.05.

RESULTS

The highest precipitation values were observed in March/2017 for both reservoirs, with the supereutrophic reaching 156.2 mm and the mesotrophic at 17.5 mm. In the mesotrophic reservoir, the highest water transparency was recorded in July/2016 compared to the supereutrophic reservoir, with high Secchi disc value (2.2 m). The pH ranged from 4.4 (acid) to 12.5 (alkaline) between July/2016 and January/2017 in the mesotrophic reservoir, while water pH remained alkaline in the supereutrophic reservoir, ranged from 7.8 to 12.6 (Table I). In addition to pH, other variables, such as water temperature, conductivity, total dissolved solids and nutrients (nitrite and nitrate) varied over time in both reservoirs (Table II). Ammoniacal nitrogen, dissolved inorganic nitrogen, and total phosphorus showed temporal variation in the mesotrophic reservoir only (Table II).

Table I
Limnological variables of the mesotrophic (Tabocas) and supereutrophic (Tapacurá) reservoirs between the depths and months. Sur = surface; Z_eu = euphotic zone limit; Bot = bottom. Not analyzed (-). Z_max = Maximum depth (m); Z_eu = Depth of euphotic zone; Z_eu:Z_max = Ratio between euphotic zone:maximum depth; Transparency = Water transparency (cm); WT = Water temperature (°C); Luminous intensity (µmol photons m^-2 s^-1); DO = Dissolved oxygen (mg L^-1); EC = Electrical conductivity (µS cm^-1); TDS = Total dissolved solids (mg L^-1); NO₃ ^- = Nitrate (µg L^-1); NO₂ ^- = Nitrite (µg L^-1); NH₄ ⁺ = Ammoniacal nitrogen (µg L^-1); DIN = Dissolved inorganic nitrogen (µg L^-1); PO₄ ^3- = Orthophosphate (µg L^-1); TP = Total phosphorus (µg L^-1).

Variables	Mesotrophic Reservoir												Supereutrophic Reservoir
	July/2016			September/2016			January/2017			March/2017			July/2016			September/2016			January/2017			March/2017
	Sur	Zeu	Bot	Sur	Zeu	Bot	Sur	Zeu	Bot	Sur	Zeu	Bot	Sur	Zeu	Bot	Sur	Zeu	Bot	Sur	Zeu	Bot	Sur	Zeu	Bot
Zmax	-	-	4.0	-	-	1.6	-	-	1.3	-	-	1.9	-	-	10.0	-	-	11.4	-	-	8.6	-	-	8.8
Transparency	2.2	-	-	0.6	-	-	0.15	-	-	0.5	-	-	1.2	-	-	0.7	-	-	0.7	-	-	0.6	-	-
Zeu	2.2	-	-	1.6	-	-	0.4	-	-	1.35	-	-	3.2	-	-	1.9	-	-	1.9	-	-	1.6	-	-
Zeu:Zmax	0.6	-	-	1.0	-	-	0.3	-	-	0.7	-	-	0.3	-	-	0.2	-	-	0.2	-	-	0.2	-	-
WT	25.3	24.4	22.5	27.8	27.5	26.9	29.5	29.3	25.8	26.7	26.3	25.5	26.9	26.8	26.6	28.5	28.0	26.7	29.5	29.4	28.5	30.5	30.5	29.0
Luminous intensity	-	-	-	522.8	65.6	17.1	525.5	4.5	0.4	516.4	2.7	3.3	655.1	7.2	5.3	380.7	2.4	1.8	297.7	2.6	1.6	655.9	7.1	4.3
DO	6.0	5.5	4.3	5.8	5.4	4.5	9.3	9.7	5.0	3.7	3.9	4.1	4.8	3.5	0.0	7.9	4.7	0.0	4.8	3.8	2.5	5.4	5.00	0.0
pH	4.5	4.4	4.6	8.4	8.7	8.7	11.4	11.5	12.5	10.1	10.2	11.1	8.0	7.9	7.8	9.2	8.5	8.3	9.9	8.9	7.9	12.6	12.2	11.5
EC	95	94	87	120	132	125	208	207	210	228	227	225	410	410	414	425	425	423	483	483	479	540	538	516
TDS	47	47	43	60	66	63	104	104	105	114	113	113	205	205	207	213	213	211	241	241	240	270	269	258
NO3 -	41.5	43.3	34.7	58.4	34.4	30.0	7.8	16.6	24.8	543.9	675.4	619.8	150.0	130.6	111.8	15.4	14.2	19.8	0.1	2.1	4.3	0.8	1.9	1.9
NO2 -	0.3	2.3	1.7	2.3	2.0	1.2	4.3	3.8	4.0	210.9	223.6	206.3	7.2	8.4	8.9	4.3	7.8	1.4	1.4	1.7	1.7	1.4	1.2	1.2
NH4 +	41.3	13.4	54.7	250.8	239.4	279.7	273.5	163.1	83.6	2472.7	2379.8	2569.7	162.0	160.0	186.3	19.6	16.5	221.9	23.7	67.1	28.9	18.6	132.1	55.7
DIN	83.0	59.0	91.1	311.5	275.9	310.8	285.6	183.6	112.5	3227.4	3278.8	3395.8	319.2	298.9	307.0	39.3	38.5	243.1	25.3	70.9	35.0	20.9	135.1	58.8
PO4 3-	6.6	14.9	10.0	26.6	38.2	14.9	8.3	31.5	46.5	6.6	14.9	8.3	664.0	670.6	1020.9	654.0	707.2	715.5	788.5	791.8	780.2	859.9	720.4	823.4
TP	136.0	111.9	154.2	114.9	42.3	102.8	193.5	172.3	269.1	347.7	450.4	399.0	885.7	879.7	1641.5	931.1	946.2	973.4	976.4	1030.8	1046.0	1094.3	1136.6	1121.5

Table II
Statistical values of ANOVA two-way of limnological variables and zooplankton biomass (μg PS m^-3) and phytoplanktonic classes (mg L^-1) between the depths (surface, Z_eu and bottom) and the months studied (July, September, January and March) in the mesotrophic (Tabocas) and supereutrophic (Tapacurá) reservoirs. Numbers in bold represent significant values (p < 0.05). Z_max = Maximum depth (m); Transparency = Water transparency (cm); Z_eu = Depth of euphotic zone; Z_eu:Z_max = Ratio between euphotic zone:maximum depth; WT = Water temperature (°C); EC = Electrical conductivity (µS cm^-1); Luminous intensity (µmol photons m^-2 s^-1); TDS = Total dissolved solids (mg L^-1); DO = Dissolved oxygen (mg L^-1); NO₂ ^- = Nitrite (µg L^-1); NO₃ ^- = Nitrate (µg L^-1); NH₄ ⁺ = Ammoniacal nitrogen (µg L^-1); DIN = Dissolved inorganic nitrogen (µg L^-1); PO₄ ^3- = Orthophosphate (µg L^-1); TP = Total phosphorus (µg L^-1).

Variables	Mesotrophic Reservoir						Supereutrophic Reservoir
	Depths			Months			Depths			Months
	Df	F	p	Df	F	p	Df	F	p	Df	F	p
Zmax	2	1.37	0.303	3	3.25	<0.05	2	0.75	0.501	3	0.13	0.93
Transparency				3	6.18	<0.05				3	7.18	<0.05
Zeu				3	9.61	<0.05				3	1.64	<0.05
Zeu:Zmax				3	2.19	<0.05				3	2.69	<0.05
WT				3	5.70	<0.05				3	12.06	<0.05
EC	2	0.00	0.997	3	836.2	<0.05	2	0.02	0.983	3	193.9	<0.05
Luminous intensity	2	5.6	0.06	2	0.01	0.992	2	134.5	<0.05	3	0.09	0.965
TDS	2	0.00	0.998	3	842.9	<0.05	2	0.02	0.983	3	193.1	<0.05
pH	2	0.05	0.995	3	169.1	<0.05	2	0.20	0.824	3	43.84	<0.05
DO	2	0.28	0.763	3	4.43	<0.05	2	19.8	<0.05	3	0.13	0.939
NO2 -	2	0.00	0.998	3	9.43	<0.05	2	0.01	0.987	3	8.51	<0.05
NO3 -	2	0.01	0.99	3	9.46	<0.05	2	0.19	0.829	3	90.32	<0.05
NH4 +	2	0.00	0.997	3	854.7	<0.05	2	0.75	0.501	3	2.07	0.287
DIN	2	0	1	3	185	<0.05	2	0.21	0.818	3	6.69	0.08
PO4 3-	2	0.91	0.437	3	2.26	0.174	2	1.46	0.283	3	4.44	0.218
TP	2	0.08	0.921	3	31.55	<0.05	2	1.60	0.255	3	4.85	0.183
Phytoplankton
Cyanophyceae	2	0.88	0.428	3	14.26	<0.05	2	4.11	<0.05	3	3.61	<0.05
Zygnematophyceae	2	2.18	0.135	3	43.13	<0.05
Bacillariophyceae	2	4.10	0.08	3	18.79	<0.05
Synurophyceae	2	0.28	0.757	3	1.55	0.229
Others	2	1.79	0.189	3	14.83	<0.05	2	0.29	0.750	3	21.72	<0.05
Zooplankton
Rotifera	2	89.06	<0.05	3	1466.19	<0.05	2	0.26	0.772	3	49.04	<0.05
Cladocera	2	2.44	0.109	3	537.13	<0.05	2	5.18	<0.05	3	58.22	<0.05
Calanoida	2	20.71	<0.05	3	187.14	<0.05	2	9.00	<0.05	3	35.36	<0.05
Cyclopoida	2	11.32	<0.05	3	1.96	0.580	2	2.09	0.351	3	20.85	<0.05
Nauplii	2	14.67	<0.05	3	24.23	<0.05	2	16.95	<0.05	3	3.25	0.354

The physical and chemical water variables varied significantly between the reservoirs (PERMANOVA: F = 0.49 and p = 0.001) throughout the study period and between the depths (surface, Z_eu, and bottom). The availability of luminous intensity and dissolved oxygen were higher at the water surface of the reservoirs. A hypoxia condition was detected at the maximum depth in the supereutrophic reservoir (Table I and II). The PCA explained 68.5% of the variation of environmental variables in the two first ordination axes (first axis: 43.66%, second axis: 24.84%) (Figure 2). Two groups with different patterns for the physical and chemical water variables were observed between the reservoirs. The first group was the mesotrophic reservoir, characterized by high values of nitrogen (ammoniacal nitrogen (eigenvalue = 7.43), nitrite (eigenvalue = 6.80), nitrate (eigenvalue = 6.89), DIN (eigenvalue = 7.35)), dissolved oxygen (eigenvalue = 1.04), luminous intensity (eigenvalue = 0.0009), and by variation in pH (eigenvalue = 0.42) and Z_eu:Z_max (eigenvalue = 9.16). The second group was the supereutrophic reservoir, characterized by high concentrations of orthophosphate (eigenvalue = 13.98), total phosphorus (eigenvalue = 11.26), total dissolved solids (eigenvalue = 12.38), high electrical conductivity (eigenvalue = 12.38), water temperature (eigenvalue = 6.61), and Z_eu (eigenvalue = 4.29) (Figure 2).

Figure 2
Principal component analysis (PCA) of environmental variables between mesotrophic (Tabocas, grey) and supereutrophic reservoirs (Tapacurá, black). TDS = Total dissolved solids; EC = Electrical conductivity; WT = Water temperature; Z_eu = Euphotic zone; Z_eu:Z_max = Ratio between euphotic zone:maximum depth; DO = Dissolved oxygen; NO₂ ^- = Nitrite; NO₃ ^- = Nitrate; NH₄ ⁺ = Ammoniacal nitrogen; DIN = Dissolved inorganic nitrogen; PO₄ ^3- = Orthophosphate; TP = Total phosphorus.

The biomass and composition of total phytoplankton (PERMANOVA: F = 34.29 and p = 0.001), besides the biomass of the classes (PERMANOVA: F = 25.00 and p = 0.001), were significantly different between the reservoirs. Zygnematophyceae represented 92% of the total average biomass in the mesotrophic reservoir (37.35 mg L^-1) (Figure 3a), with Staurastrum tetracerum Ralfs ex Ralfs representing 59% of total average biomass (24.16 mg L^-1), while Bacillariophyceae represented 3% of total average biomass with Thalassiosira sp. the most representative species (0.77 mg L^-1). In the supereutrophic reservoir, Cyanophyceae was dominant (98%) (Figure 3b), with cyanobacteria M. panniformis and P. agardhii presenting the highest values of total average biomass of 46% (68.39 mg L^-1) and 27% (39.65 mg L^-1), respectively.

Figure 3
Phytoplankton biomass in the mesotrophic (Tabocas, a) and supereutrophic (Tapacurá, b) reservoirs between July/2016 and March/2017 at different depths. Sur = surface; Z_eu = euphotic zone limit; Bot = bottom. Vertical lines represent the standard error of the mean (±SEM).

Phytoplankton biomass varied throughout the months in the mesotrophic reservoir (Table II), with higher biomass recorded in July/2016, September/2016, and January/2017 to Zygnematophyceae, Bacillariophyceae, and Cyanophyceae (Figure 3a). Significant differences in Zygnematophyceae biomass were observed between the depths in January/2017, with higher biomass in the bottom than on the surface (Tukey’s HSD, p = 0.02) and Z_eu (Tukey’s HSD, p = 0.05). In the supereutrophic reservoir, Cyanophyceae and other phytoplankton classes varied both temporally and vertically (Table II), with higher cyanobacteria biomass (September/2016 and January/2017) (Figure 3b).

In the mesotrophic reservoir, in addition to desmids, significant differences were observed in the biomass of diazotrophic filamentous (Kruskal-Wallis, H = 12.962 e p = 0.004) and non-diazotrophic (F = 13.152 and p <0.0001) cyanobacteria and colonial cyanobacteria (Kruskal-Wallis, H = 10.482 and p = 0.01) between months. Higher biomass was observed in July/2016 and January/2017 for non-diazotrophic filamentous cyanobacteria (Figure 4a), with dominance of the species Anagnostidinema (previously Geitlerinema) amphibium (C.Agardh ex Gomont) Strunecký, Bohunická, J.R.Johansen & J.Komárek (Strunecky et al. 2017). From diazotrophic and colonial cyanobacteria, R. raciborskii and Aphanocapsa elachista West & G.S.West were dominant, respectively.

Figure 4
Biomass of diazotrophic and non-diazotrophic filamentous and colonial cyanobacteria in the mesotrophic (Tabocas; a) and supereutrophic (Tapacurá; b) reservoirs between July/2016 and March/2017 at different depths. Sur = surface; Z_eu = euphotic zone limit; Bot = bottom. Vertical lines represent the standard error of the mean (±SEM).

In the supereutrophic reservoir, diazotrophic filamentous cyanobacteria differed between months (F = 6.814 and p = 0.002), as well as non-diazotrophic cyanobacteria (F = 58.810 and p <0.0001), presenting higher biomass in March/2017 and September/2016, respectively (Figure 4b). Significant variation in the colonial cyanobacteria biomass was observed (F = 18.732 and p <0.0001), with higher biomass recorded in January and March/2017 (Figure 4b). In July/2016, codominance was observed between the non-diazotrophic colonial and filamentous species of cyanobacteria. In September/2016, the filamentous species P. agardhii was dominant, and in the other months, the colonial species M. panniformis was dominant (Figure 4b). No variation in cyanobacteria biomass was observed between depths.

The RDA explained 78% (F = 2.91 and p = 0.001) of the phytoplankton distribution in the mesotrophic and supereutrophic reservoirs, with the axes 1 and 2 representing 46% (p = 0.002) and 32% (p = 0.001) of the distribution, respectively. The dissolved oxygen (p = 0.01), Z_eu:Z_max (p = 0.002), DIN (p = 0.02), and biomass of nauplii (p = 0.05) significantly influenced phytoplankton species in the mesotrophic reservoir, and in the supereutrophic reservoir, the RDA showed that non-diazotrophic filamentous cyanobacteria (P. agardhii and Planktothrix isothrix (Skuja) Komárek & Komárková), diazotrophic filamentous cyanobacteria (R. raciborskii), and colonial cyanobacteria (M. panniformis and M. aeruginosa) were positively influenced by nitrogen (DIN), luminous intensity (p = 0.01), Z_eu (p = 0.03), pH (p = 0.005), water temperature (p = 0.005), electrical conductivity (p = 0.005), total phosphorus (p = 0.003), PO₄ ^3- (p = 0.005), total dissolved solids (p = 0.001), and biomass of Cyclopoida (p = 0.01) (Figure 5a).

Figure 5
Redundancy Analysis (RDA) of phytoplankton (a) and zooplankton (b) in the mesotrophic (Tabocas, grey) and supereutrophic (Tapacurá, black) reservoirs. Geometric shapes represent months of study: triangles (July), circles (September), rhombuses (January) and squares (March). The filling of the shapes represents the depths: solid line (surface), traced line (Z_eu) and all black (bottom). TDS = Total dissolved solids; EC = Electrical conductivity; WT = Water temperature; Z_eu = Euphotic zone; Z_eu:Z_max = Ratio between euphotic zone:maximum depth; DO = Dissolved oxygen; NO₂ ^- = Nitrite; NO₃ ^- = Nitrate; DIN = Dissolved inorganic nitrogen; PO₄ ^3- = Orthophosphate; TP = Total phosphorus; Plankghii = Planktothrix agardhii; Plnkthrix = Planktothrix isothrix; Raphi = Raphidiopsis raciborskii; Micaerug = Microcystis aeruginosa; Micpann = Microcystis panniformis; Micprotis = Microcystis protocystis; Anag = Anagnostidinema amphibium; Stamsp = Sataurastrum sp.; Stamtet = Staurastrum tetracerum; Pseucate = Pseudanabaena catenata; Euastabm = Euastrum abruptum.

The biomass and composition of total zooplankton (PERMANOVA: F = 11.31 and p = 0.001) and the biomass of the groups (PERMANOVA: F = 7.96 and p = 0.001) were significantly different between the reservoirs. The zooplankton groups showed variation in biomass between months and depths in both reservoirs (Table II). Copepoda Calanoida was dominant in the mesotrophic and supereutrophic reservoir, contributing to 67% (312.98 µg DW^-3) and 59% (222.33 µg DW^-3) of the total average biomass of zooplankton, respectively (Figure 6a, b).

Figure 6
Zooplankton biomass in the mesotrophic (Tabocas, a) and supereutrophic (Tapacurá, b) reservoirs between July/2016 and March/2017 at different depths. Sur = surface; Z_eu = euphotic zone limit; Bot = bottom. Vertical lines represent the standard error of the mean (±SEM).

The RDA explained 65.47% (F = 9.45 and p = 0.007) of the relationship between zooplankton with abiotic variables and phytoplankton of the mesotrophic and supereutrophic reservoirs. The axis 1 represented 54.11% (p = 0.001) of the distribution of variables, while axis 2 represented 11.36% (p = 0.03). Staurastrum tetracerum (p = 0.05), Staurastrum sp. (p = 0.03), Euastrum abruptum Nordstedt (p = 0.01), nitrate (p = 0.04), nitrite (p = 0.03), DIN (p = 0.02), and luminous intensity (p = 0.04) positively influenced Calanoida biomass in the mesotrophic reservoir, while in the supereutrophic reservoir Cyclopoida biomass was positively related to water temperature (p = 0.005), pH (p = 0.02), and the biomass of Pseudanabaena catenata Lauterborn (p = 0.02), A. amphibium (p = 0.02), and M. aeruginosa (p = 0.05) (Figure 5b).

DISCUSSION

Our study showed that nutrients play an important role in structuring phytoplankton and zooplankton communities since nitrogen forms created two distinct scenarios in reservoirs: one with high phosphorus concentrations (supereutrophic) and another with high nitrogen concentrations (mesotrophic). Our first hypothesis was partially accepted since the phytoplankton biomass differed temporally and vertically only in the supereutrophic reservoir, while in the mesotrophic reservoir only the temporal variation caused significant changes in phytoplankton. Also, the dominance of non-diazotrophic filamentous cyanobacteria was observed under high concentrations of nitrogen in mesotrophic (throughout the study period) and supereutrophic (July/2016 and September/2016) reservoir. Moreover, the zooplankton biomass differed between the reservoirs, which confirms our second hypothesis that cyanobacteria biomass negatively influence the Calanoida copepods, while the chlorophytes positively influence their biomass.

In the supereutrophic reservoir, cyanobacteria blooms were observed throughout the study period. In the past few decades, studies conducted in reservoirs from Pernambuco, Northeastern Brazil, have detected perennial cyanobacteria blooms with monospecific dominance (Moura et al. 2018). Most studies indicate the dominance of Cylindrospermopsis, Microcystis, and Planktothrix in the blooms (Bouvy et al. 2000, Moura et al. 2018). There is strong evidence that temperature and nutrients are the main factors contributing to the frequent occurrences of cyanobacteria blooms in tropical reservoirs (Rigosi et al. 2014).

The codominance and substitution of non-diazotrophic filamentous cyanobacteria, mainly P. agardhii, by colonial cyanobacteria of the Microcystis genus was observed in the supereutrophic reservoir, showing a direct relationship with nitrogen availability and increased electrical conductivity and water temperature over the months. In colder conditions, P. agardhii can grow and remain in the environment for long periods (Mantzouki et al. 2016), besides growing under high phosphorus concentrations (Aguilera et al. 2019). In the supereutrophic reservoir, we observed the growth of P. agardhii when DIN, nitrate, and nitrite presented high availability, however, in low availability, the biomass of P. agardhii reduced. Other studies conducted in the Tapacurá reservoir have shown that, in addition to nitrogen and phosphorus concentrations, the mixing zone (Z_mix), turbidity and Z_max contributed to the success and variation of cyanobacteria composition during seasonal changes (Dantas et al. 2012, Diniz et al. 2019).

The Z_eu and Z_eu:Z_max showed a positive relationship between diazotrophic and non-diazotrophic filamentous cyanobacteria, and an inverse relationship with colonial cyanobacteria in the supereutrophic reservoir, similar to that observed in other studies conducted in Southeastern and Northeastern Brazil (Bortolini et al. 2016, Costa et al. 2016). Dantas et al. (2012) showed that colonial cyanobacteria were favored by high phosphorus concentrations during the rainy season, coinciding with smaller Z_eu and lower biomass of filamentous cyanobacteria, which were favored by greater light penetration in the water column (Z_eu:Z_max) in the Jucazinho hypereutrophic reservoir, located in Pernambuco State, Brazil.

The increase in temperature and nutrients favored cyanobacteria blooms by M. aeruginosa and M. panniformis. Paerl & Otten (2013) and Shan et al. (2019) report the dominance of Microcystis in shallow lakes with high temperatures since the temperature can interact synergistically with the high phosphorus values and favor Microcystis spp. blooms. The formation of large Microcystis colonies, associated with turbid waters, is a factor that inhibits the growth of filamentous cyanobacteria, as it limits light and competition for nutrients (Paerl et al. 2016, Shan et al. 2019). These factors explain why the Microcystis biomass was higher in months with higher temperatures and phosphorus availability, followed by lower nitrogen concentrations. Studies point out that phytoplankton growth is only limited by phosphorus (Schindler et al. 2008, Spears et al. 2012). However, our results showed that nitrogen availability is an important factor in the competitive relationship between filamentous and colonial non-diazotrophic cyanobacteria species and plays an important role in cyanobacteria blooms under limited conditions.

The second scenario observed, with high nitrogen values (mainly ammoniacal nitrogen and DIN), pH variation, and dense banks of E. densa verified in the mesotrophic reservoir, favored the dominance of desmids. In freshwater environments with low nutrient values and the presence of macrophytes, desmids have high diversity and biomass (Borics et al. 2003, Ngearnpat & Peerapornpisal 2007). The temporal variation, and possibly death and decomposition of macrophytes, were responsible for the variation in the physical and chemical water characteristics throughout the study, which changed the phytoplankton community.

Macrophytes are responsible for the maintenance and functioning of lakes and reservoirs since they accumulate various forms of nutrients available in the environment throughout their lifecycles (Kissoon et al. 2013). However, the death and decomposition of macrophytes release high loads of ammoniacal nitrogen back into the environment, which may favor specific phytoplankton groups (Bellisario et al. 2012). High concentrations of ammoniacal nitrogen in the environment through macrophyte senescence, especially in very shallow lakes, can be toxic to aquatic organisms (Farnsworth-Lee & Baker 2000). The results of the present study showed that typically planktonic desmids, such as S. tetracerum and Staurastrum sp., were dominant with high concentrations of ammoniacal nitrogen caused by the death of the dense E. densa banks.

Although desmid contributed to more than 90% of total average biomass in the mesotrophic reservoir, non-diazotrophic cyanobacteria and other algae such as diatoms and synurophytes were important in the phytoplankton biomass throughout the study period. Phytoplankton composition in mesotrophic reservoirs is usually made up of green algae, diatoms, and flagellates, which are sensitive to changes in nutrient concentrations and physical variables caused by seasonal variations (Oliveira et al. 2020). In the present study, diatoms presented higher biomass under conditions of high water transparency and Z_eu, and low nutrient concentrations, similar to what Hu et al. (2016) observed in a tropical reservoir, where diatoms were influenced by improved water quality.

In addition to nitrogen, the pH variation (from acid to alkaline) caused changes in the structure of the phytoplankton community in the mesotrophic reservoir. Desmids are commonly found in several environments with low pH (Lenzenweger 2000), however, more recent studies have detected these algae in waters with pH ranging from neutral to alkaline (Ngearnpat & Peerapornpisal 2007, Mataloni et al. 2015). This allows us to infer that desmids have a wide range of adaptations to trophic conditions in an environment. Therefore, the presence of E. densa and high pH may explain the dominance of desmids in the mesotrophic reservoir.

In the supereutrophic reservoir, the high pH (8-12) was positively related to cyanobacteria. Such relationship was also observed by Fernandes et al. (2009), who emphasized the advantage of cyanobacteria in assimilating bicarbonate within the medium, making them more competitive than other algae. The conversion of bicarbonate into carbon dioxide by the carbonic anhydrase enzyme occurs within the cell through carbon concentration mechanisms, and the release of OH - into the medium, which results in increased pH (Ataeian et al. 2019). Furthermore, even under ideal nutrient conditions, high pH is an indispensable requirement for cyanobacterial growth (Unrein et al. 2010, Visser et al. 2016).

Zooplankton varied temporally and vertically in the supereutrophic and mesotrophic reservoirs. The temporal and vertical variation in the zooplankton composition and biomass was influenced by temperature, dissolved oxygen concentrations and food availability (Domis et al. 2013, Silva et al. 2018), with zooplankton being more abundant in the water column with a higher concentration of algae (Keppeler & Hardy 2004). In the present study, higher zooplankton biomass was observed in Z_eu, where higher phytoplankton biomass was recorded. Besides, the vertical migration of zooplankton is different for each group, which is related to the mechanisms of feeding, physiology, and adaptation to the variation of abiotic factors, such as temperature (Ermolaeva et al. 2019) and defense mechanisms against predation (Picapedra et al. 2015).

Calanoida was predominant during the study and at different depths and showed a direct relationship with the phytoplankton composition in both reservoirs. The predominance of Calanoida in tropical eutrophic reservoirs with perennial cyanobacteria blooms has recently been recorded (De-Carli et al. 2018, Diniz et al. 2019). Although cyanobacteria are considered nutritionally poor and difficult for zooplankton to ingest due to their large size and toxin production (Kruk et al. 2016), Calanoida copepods can consume these organisms (Colina et al. 2016, Diniz et al. 2019). In an experiment, Leitão et al. (2018) observed that the calanoid copepod Notodiaptomus iheringi (Wright S., 1935) reduced the biomass of Cryptomonas sp. and did not affect Microcystis sp., because calanoids select eukaryotic algae, which reduces competition with cyanobacteria and facilitates flowering, especially for Microcystis. The selective capacity of calanoids is based on mechanical and chemical perception, as they can detect, ingest or reject prey based on size, motility, and nutritional value (Henriksen et al. 2007, Tiselius et al. 2013).

In the mesotrophic reservoir, desmids positively influenced calanoids. Green algae contain a high level of alpha-linolenic fatty acids (ALA) (Taipale et al. 2013), and, therefore, are a nutritious food source for zooplankton. Moreover, a positive relationship was observed between Cyclopoida and cyanobacteria in the supereutrophic reservoir, which can be explained by food selectivity. Gebrehiwot et al. (2019) observed that the cyclopoid copepod Thermocyclops decipiens (Kiefer, 1929) preferred to consume diatoms rather than the cyanobacteria R. raciborskii. Such food selectivity for smaller sized prey with greater nutritional value favors the coexistence and dominance of cyanobacteria (Hong et al. 2013, Rangel et al. 2016).

Unlike Calanoida, rotifers showed low biomass in the supereutrophic reservoir when compared to the mesotrophic reservoir. Rotifers can directly ingest small-sized cyanobacteria in low biomass (Geng & Xie 2008). However, cyanobacteria blooms dominated by Microcystis result in reduced rotifer biomass, as seen in other studies (Soares et al. 2010, Ji et al. 2017). Decreased rotifer biomass can be explained by the predominance of Microcystis in the supereutrophic reservoir, while increased rotifers in the mesotrophic reservoir was influenced by the greater availability of palatable algae, such as desmids and euglenophytes. In general, green algae are considered food sources with high nutritional value and are easily ingested by zooplankton (Fragoso et al. 2009), providing a greater diversity of zooplankton species (Colina et al. 2016).

Our study showed that the effects of temporal variation on the composition and structure of phytoplankton and zooplankton communities in the mesotrophic and eutrophic reservoirs were associated with environmental factors, mainly ammoniacal nitrogen, DIN and water temperature. The vertical variation only changed the zooplankton community, since they respond ecologically to abiotic and biotic factors, in this case, phytoplankton. Planktothrix agardhii, M. panniformis, and M. aeruginosa showed different strategic behaviors in response to environmental variations and nutrient availability, mainly from available nitrogen forms. Under mesotrophic conditions, excessive availability of ammoniacal nitrogen in the water, possibly resulting from the death and decomposition of the submerged macrophyte E. densa, favored the dominance of desmids (especially S. tetracerum) in an environment considered toxic to most aquatic organisms.

Copepoda Calanoida showed a direct relationship with greater availability of palatable algae (Zygnematophyceae) in the mesotrophic reservoir, contrary to what was observed in the supereutrophic reservoir, where phytoplankton algae did not influence the zooplankton groups. Finally, this study supports the need to better understand trophic relationships on a temporal scale through variation in nitrogen forms that act directly on the phytoplankton community in tropical reservoirs, regardless of the trophic state. We emphasize the importance of nitrogen in management strategies for tropical reservoirs, as well as the adaptability of Copepoda Calanoida to different trophic conditions and phytoplankton compositions.

ACKNOWLEDGMENTS

The Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for the productivity grant to ANM (Process 305829/2019-0), and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brazil (CAPES) for the financial support through the granting scholarship of ASD.

REFERENCES

• AGUILERA A, AUBRIOT L, ECHENIQUE RO, DONADELLI JL & SALERNO GL. 2019. Raphidiopsis mediterranea (Nostocales) exhibits a flexible growth strategy under light and nutrient fluctuations in contrast to Planktothrix agardhii (Oscillatoriales). Hydrobiologia 839: 145-157.
• AGUILERA A, BERRENDERO GÓMEZ E, KASTOVSKY J, ECHENIQUE RO & SALERNO GL. 2018. The polyphasic analysis of two native Raphidiopsis isolates supports the unification of the genera Raphidiopsis and Cylindrospermopsis (Nostocales, Cyanobacteria). Phycologia 57: 130-146.
• ALVARES CA, STAPE JL, SENTELHAS PC, DE MORAES G, LEONARDO J & SPAROVEK G. 2013. Köppen’s climate classification map for Brazil. Meteorol Z 22: 711-728.
• ANNEVILLE O, GAMMETER S & STRAILE D. 2005. Phosphorus decrease and climate variability: mediators of synchrony in phytoplankton changes among European peri-alpine lakes. Freshw Biol 50: 1731-1746.
• APAC. 2017. Agência Pernambucana de Águas e Climas. Available in: http://www.apac.pe.gov.br/ (Accessed September, 2017).
  » http://www.apac.pe.gov.br/
• ATAEIAN M, LIU Y, CANON-RUBIO KA, NIGHTINGALE M, STROUS M & VADLAMANI A. 2019. Direct capture and conversion of CO2 from air by growing a cyanobacterial consortium at pH up to 11.2. Biotechnol Bioeng 116: 1604-1611.
• BELLISARIO B, CERFOLLI F & NASCETTI G. 2012. The interplay between network structure and functioning of detritus-based communities in patchy aquatic environment. Aquat Ecol 46: 431-441.
• BORICS G, TÓTHMÉRÉSZ B, GRIGORSZKY I, PADISÁK J, VÁRBÍRÓ G & SZABÓ S. 2003. Algal assemblage types of bog-lakes in Hungary and their relation to water chemistry, hydrological conditions and habitat diversity. In: Naselli-Flores L, Padisák J and Dokulil M. (Eds), Phytoplankton and Equilibrium Concept: The Ecology of Steady-State Assemblages, Dordrencht: Springer, p. 145-155.
• BORTOLINI JC, DA SILVA PRL, BAUMGARTNER G & BUENO NC. 2019. Response to environmental, spatial, and temporal mechanisms of the phytoplankton metacommunity: comparing ecological approaches in subtropical reservoirs. Hydrobiologia 830: 45-61.
• BORTOLINI JC, MORESCO GA, DE PAULA ACM, JATI S & RODRIGUES LC. 2016. Functional approach based on morphology as a model of phytoplankton variability in a subtropical floodplain lake: a long-term study. Hydrobiologia 767: 151-163.
• BOUVY M, FALCÃO D, MARINHO M, PAGANO M & MOURA A. 2000. Occurrence of Cylindrospermopsis (Cyanobacteria) in 39 Brazilian tropical reservoirs during the 1998 drought. Aquat Microb Ecol 23: 13-27.
• CAO HS, KONG FX, LUO LC, SHI XL, YANG Z, ZHANG XF & TAO Y. 2006. Effects of wind and wind-induced waves on vertical phytoplankton distribution and surface blooms of Microcystis aeruginosa in Lake Taihu. J Freshw Ecol 21: 231-238.
• CHAFFIN JD, BRIDGEMAN TB & BADE DL. 2013. Nitrogen constrains the growth of late summer cyanobacterial blooms in Lake Erie. Adv Microbiol 3: 16-26.
• COLINA M, CALLIARI D, CARBALLO C & KRUK C. 2016. A trait-based approach to summarize zooplankton–phytoplankton interactions in freshwaters. Hydrobiologia 767: 221-233.
• COSTA MRA, ATTAYDE JL & BECKER V. 2016. Effects of water level reduction on the dynamics of phytoplankton functional groups in tropical semi-arid shallow lakes. Hydrobiologia 778: 75-89.
• DANTAS ÊW, BITTENCOURT-OLIVEIRA MC & MOURA AN. 2012. Dynamics of phytoplankton associations in three reservoirs in northeastern Brazil assessed using Reynolds’ theory. Limnologica 42: 72-80.
• DAVIS TW, BULLERJAHN GS, TUTTLE T, MCKAY RM & WATSON SB. 2015. Effects of increasing nitrogen and phosphorus concentrations on phytoplankton community growth and toxicity during Planktothrix blooms in Sandusky Bay, Lake Erie. Environ Sci Technol 49: 7197-7207.
• DE-CARLI BP, ALBUQUERQUE FPD, MOSCHINI-CARLOS V & POMPÊO M. 2018. Comunidade zooplanctônica e sua relação com a qualidade da água em reservatórios do Estado de São Paulo. Iheringia Ser Zool 108: 1-11.
• DINIZ AS, SEVERIANO JS, MELO JÚNIOR M, DANTAS ÊW & MOURA AN. 2019. Phytoplankton–zooplankton relationships based on phytoplankton functional groups in two tropical reservoirs. Mar Freshwater Res 70: 721-733.
• DOMIS SLN ET AL. 2013. Plankton dynamics under different climatic conditions in space and time. Freshw Biol 58: 463-482.
• DONG J, ZHOU Q, GAO Y, GU Q, LI G & SONG L. 2018. Long-term effects of temperature and nutrient concentrations on the phytoplankton biomass in three lakes with differing trophic statuses on the Yungui Plateau, China. Ann Limnol-Int J Lim 54: 1-9.
• DOWNING JA, WATSON SB & MCCAULEY E. 2001. Predicting Cyanobacteria dominance in lakes. Can J Fish Aquat Sci 58: 1905-1908.
• DUMONT HJ, VAN DE VELDE I & DUMONT S. 1975. The dry weight estimate of biomass in a selection of Cladocera, Copepoda and Rotifera from the plankton, periphyton and benthos of continental waters. Oecol 19: 75-97.
• ELLIOTT JA. 2012. Predicting the impact of changing nutrient load and temperature on the phytoplankton of England’s largest lake, Windermere. Freshw Biol 57: 400-413.
• ELMOOR-LOUREIRO LMA. 1997. Manual de identificação de cladóceros límnicos do Brasil. Brasília: Editora Universa, 156 p.
• ERMOLAEVA NI, ZARUBINA EY, BAZHENOVA OP, DVURECHENSKAYA SY & MIKHAILOV VV. 2019. Influence of abiotic and trophic factors on the daily horizontal migration of zooplankton in the littoral zone of the Novosibirsk reservoir. Inland Water Biol 12: 418-427.
• ESTEVES FDA. 2011. Fundamentos de Limnologia, 3ª ed., Rio de Janeiro: Interciência, 790 p.
• FARNSWORTH-LEE LA & BAKER LA. 2000. Conceptual model of aquatic plant decay and ammonia toxicity for shallow lakes. J Environ Eng 126: 199-207.
• FERNANDES VO, CAVATI B, OLIVEIRA LB & SOUZA BD. 2009. Ecologia de cianobactérias: fatores promotores e consequências das florações. Oecol Bras 13: 247-258.
• FRAGOSO JR CR, FERREIRA TF & DA MOTTA MARQUES D. 2009. Modelagem ecológica em ecossistemas aquáticos, 1ª ed., Brasil: Oficina de textos, 304 p.
• GEBREHIWOT M, KIFLE D & TRIEST L. 2019. Grazing and growth rate of a cyclopoid copepod fed with a phytoplankton diet constituted by a filamentous cyanobacterium. Hydrobiologia 828: 213-227.
• GENG H & XIE P. 2008. Experimental studies on the effects of toxic Microcystis aeruginosa PCC7820 on the survival and reproduction of two freshwater rotifers Brachionus calyciflorus and Brachionus rubens. Ecotoxicology 17: 709-715.
• GONZÁLEZ EJ & ROLDÁN G. 2019. Eutrophication and phytoplankton: Some generalities from lakes and reservoirs of the americas. Phytoplankton Ecology and Dynamics: IntechOpen.
• HAMPTON SE, GRAY DK, IZMEST’EVA LR, MOORE MV & OZERSKY T. 2014. The rise and fall of plankton: long-term changes in the vertical distribution of algae and grazers in Lake Baikal, Siberia. PloS One 9: 1-10.
• HANSSON LA & HYLANDER S. 2009. Size-structured risk assessments govern Daphnia migration. Proceedings of the Royal Society B: Biol Sci 276: 331-336.
• HARKE MJ, STEFFEN MM, GOBLER CJ, OTTEN TG, WILHELM SW, WOOD SA & PAERL HW. 2016. A review of the global ecology, genomics, and biogeography of the toxic cyanobacterium, Microcystis spp. Harmful Algae 54: 4-20.
• HENRIKSEN CI, SAIZ E, CALBET A & HANSEN BW. 2007. Feeding activity and swimming patterns of Acartia grani and Oithona davisae nauplii in the presence of motile and non-motile prey. Mar Ecol Prog Ser 331: 119–129.
• HILLEBRAND H, DÜRSELEN CD, KIRSCHTEL D, POLLINGHER U & ZOHARY T. 1999. Biovolume calculation for pelagic and benthic microalgae. J Phycol 35: 403-424.
• HONG Y, BURFORD MA, RALPH PJ, UDY JW & DOBLIN MA. 2013. The cyanobacterium Cylindrospermopsis raciborskii is facilitated by copepod selective grazing. Harmful Algae 29: 14-21.
• HU R, LI Q, HAN BP, NASELLI-FLORES L, PADISAK J & SALMASO N. 2016. Tracking management-related water quality alterations by phytoplankton assemblages in a tropical reservoir. Hydrobiologia, 763: 109-124.
• INMET. 2017. Instituto Nacional de Meteorologia. Available in: http://www.inmet.gov.br/portal/ (Accessed September, 2017).
  » http://www.inmet.gov.br/portal/
• JENSEN P, JEPPESEN E, OLRIK K & KRISTENSEN P. 1994. Impact of nutrients and physical factors on the shift from cyanobacterial to chlorophyte dominance in shallow Danish lakes. Can J Fish Aquat Sci 51: 1692-1699.
• JEPPESEN E ET AL. 2010. Impacts of climate warming on lake fish community structure and potential effects on ecosystem function. Hydrobiologia 646: 73-90.
• JI G, HAVENS KE, BEAVER JR & FULTON RS. 2017. Response of zooplankton to climate variability: Droughts create a perfect storm for Cladocerans in shallow eutrophic lakes. Water 9: 1-20.
• JOHN DM, WHITON BA & BROOK AJ. 2002. The freshwater algal flora of the British Isles: an identification guide of freshwater and terrestrial algae. Cambridge: Cambridge University Press, 703 p.
• KEPPELER EC & HARDY ER. 2004. Vertical distribution of zooplankton in the water column of Lago Amapá, Rio Branco, Acre, Brazil. Rev Bras Zool 21: 169-177.
• KISSOON LTT, JACOB DL, HANSON MA, HERWIG BR, BOWE SE & OTTE ML. 2013. Macrophytes in shallow lakes: Relationships with water, sediment and watershed characteristics. Aquat Bot 109: 39-48.
• KOEL TM, TRONSTAD LM, ARNOLD JL, GUNTHER KA, SMITH DW, SYSLO JM & WHITE PJ. 2019. Predatory fish invasion induces within and across ecosystem effects in Yellowstone National Park. Sci Adv 5: eaav1139.
• KOMÁREK J & ANAGNOSTIDIS K. 1999. Cyanoprokaryota, Chroococcales. Stuttgart: New York: Gustav Fisher Verlag, 548 p.
• KOMÁREK J & ANAGNOSTIDIS K. 2005. Cyanoprokaryota 2. Teil/2nd part: Oscillatoriales. n. 19: Susswasserflora von mitteleuropa, 759 p.
• KOMÁREK J. 2013. Cyanoprokaryota: Heterocytous Genera, 3 ed., Berlin: Springer Spektrum, 1130 p.
• KOROLEFF F. 1976. Determination of nutrients. In: GRASSHOFF K. (Eds), Methods of Seawater Analysis, Weinheim: Verlag Chemie, Germany, p. 82-177.
• KOSTE W. 1978. Rotatoria. Die Rädertiere Mitteleuropas, begründet von Max Voigt. Überordnung Monogononta. Berlin, Stuttgart: Gebrüder Borntraeger, 673 p.
• KOSTEN S ET AL. 2012. Warmer climates boost cyanobacterial dominance in shallow lakes. Glob Change Biol 18: 118-126.
• KRAMMER K & LANGE-BERTALOT H. 1991. Bacillariophyceae 3 Centrales, Fragilariaceae, Eunotiaceae. Susswaser flora von Mitteleuropa, Stutgart: Gustav Fischer, 576 p.
• KRUK C, SEGURA AM, COSTA LS, LACEROT G, KOSTEN S, PEETERS ETHM, HUSZAR VLM, MAZZEO N & SCHEFFER M. 2016. Functional redundancy increases towards the tropics in lake phytoplankton. J Plankton Res 39: 518-530.
• LEITÃO E, GER KA & PANOSSO R. 2018. Selective grazing by a tropical copepod (Notodiaptomus iheringi) facilitates Microcystis dominance. Front Microbiol 9: 301.
• LENZENWEGER R. 2000. Vorläufiges Ergebnis der Untersuchungen zur Zieralgenflora der Schwemm bei Walchsee in Nordtirol. Ber Naturwiss-med Ver Innsb 87: 41-66.
• LOBO E & LEIGHTON G. 1986. Communities structure of the planktonics phytocenosis in the mouth river systems of the central zone of Chile. Rev Biol Mar Oceanogr 22: 1-29.
• LÜRLING M, MELLO MME, VAN OOSTERHOUT F, DE SENERPONT DOMIS L & MARINHO MM. 2018. Response of natural cyanobacteria and algae assemblages to a nutrient pulse and elevated temperature. Front Microbiol 9: 1-14.
• MACKERETH FJH, HERON J & TALLING JF. 1978. Water analysis: some revised methods for limnologists. Freshwater Biol. Assoc. Sci. Pub: Scientific Publications, 117 p.
• MANTZOUKI E, VISSER PM, BORMANS M & IBELINGS BW. 2016. Understanding the key ecological traits of cyanobacteria as a basis for their management and control in changing lakes. Aquat Ecol 50: 333-350.
• MATALONI G, GONZÁLEZ GARRAZA G & VINOCUR A. 2015. Landscape-driven environmental variability largely determines abiotic characteristics and phytoplankton patterns in peat bog pools (Tierra del Fuego, Argentina). Hydrobiologia 751: 105-125.
• MONTÚ M & GOEDEN IM. 1986. Atlas dos Cladocera e Copepoda (Crustacea) do estuário da lagoa dos Patos (Rio Grande do Norte, Brasil). Paraná: Revista Nerítica, 134 p.
• MOURA AN, ARAGÃO-TAVARES NKC & AMORIM CA. 2018. Cyanobacterial blooms in freshwater bodies from a semiarid region, Northeast Brazil: A review. J Limnol 77: 179-188.
• NEGRO AI, DE HOYOS C & ALDASORO JJ. 2003. Diatom and desmid relationships with the environment in mountain lakes and mires of NW Spain. Hydrobiologia 505: 1-13.
• NGEARNPAT N & PEERAPORNPISAL Y. 2007. Application of desmid diversity in assessing the water quality of 12 freshwater resources in Thailand. J Appl Phycol 19: 667-674.
• OLIVEIRA SAD, FERRAGUT C & BICUDO CEM. 2020. Relationship between phytoplankton structure and environmental variables in tropical reservoirs with different trophic states. Acta Bot Bras 34: 83-93.
• PAERL HW & OTTEN TG. 2013. Harmful cyanobacterial blooms: causes, consequences, and controls. Microb Ecol 65: 995-1010.
• PAERL HW & OTTEN TG. 2016. Duelling ‘CyanoHABs’: unravelling the environmental drivers controlling dominance and succession among diazotrophic and non-N2-fixing harmful cyanobacteria. Environ Microbiol 18: 316-324.
• PAERL HW, GARDNER WS, HAVENS KE, JOYNER AR, MCCARTHY MJ, NEWELL SE, QIN B & SCOTT JT. 2016. Mitigating cyanobacterial harmful algal blooms in aquatic ecosystems impacted by climate change and anthropogenic nutrients. Harmful Algae 54: 213-222.
• PICAPEDRA P, LANSAC-TÔHA F & BIALETZKI A. 2015. Diel vertical migration and spatial overlap between fish larvae and zooplankton in two tropical lakes, Brazil. Braz J Biol 75: 352-361.
• POPOVSKÝ J & PFIESTER LA. 1990. Dinophyceae (Dinoflagellida). Sttugart: Gustav Fischer Verlag, p. 272.
• PRESCOTT GW & VINYARD WC. 1982. A Synopsis of North American Desmids. Nebraska: University of Nebraska Press, 704 p.
• R DEVELOPMENT CORE TEAM. 2015. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available in: https://www.R-project.org/
  » https://www.R-project.org/
• RANGEL LM, GER KA, SILVA LH, SOARES MCS, FAASSEN EJ & LÜRLING M. 2016. Toxicity overrides morphology on Cylindrospermopsis raciborskii grazing resistance to the calanoid copepod Eudiaptomus gracilis. Microb Ecol 71: 835-844.
• RAO K, ZHANG X, YI XJ, LI ZS, WANG P, HUANG GW & GUO XX. 2018. Interactive effects of environmental factors on phytoplankton communities and benthic nutrient interactions in a shallow lake and adjoining rivers in China. Sci Total Environ 619: 1661-1672.
• RHODE SC, PAWLOWSKI M & TOLLRIAN R. 2001. The impact of ultraviolet radiation on the vertical distribution of zooplankton of the genus Daphnia. Nature 412: 69-72.
• RIGOSI A, CAREY CC, IBELINGS BW & BROOKES JD. 2014. The interaction between climate warming and eutrophication to promote cyanobacteria is dependent on trophic state and varies among taxa. Limnol Oceanog 59: 99-114.
• RUTTNER-KOLISKO A. 1977. Comparison of various sampling techniques, and results of repeated sampling of planktonic rotifers. Arch Hydrobiol – Beih Ergebn Limnol 8: 13-18.
• SCHINDLER DW, HECKY RE, FINDLAY DL, STAINTON MP, PARKER BR, PATERSON MJ, BEATY KG, LYNG M & KASIAN SEM. 2008. Eutrophication of lakes cannot be controlled by reducing nitrogen input: Results of a 37-year whole-ecosystem experiment. Proc Nat Acad Sci 105: 11254-11258.
• SEVINDIK TO, CELIK K & NASELLI-FLORES L. 2017. Spatial heterogeneity and seasonal succession of phytoplankton functional groups along the vertical gradient in a mesotrophic reservoir. Ann Limnol-Int J Lim 53: 129-141.
• SHAN K, SONG L, CHEN W, LI L, LIU L, WU Y, JIA Y, ZHOU Q & PENG L. 2019. Analysis of environmental drivers influencing interspecific variations and associations among bloom-forming cyanobacteria in large, shallow eutrophic lakes. Harmful Algae 84: 84-94.
• SHIKATA T, MATSUNAGA S, NISHIDE H, SAKAMOTO S, ONISTUKA G & YAMAGUCHI M. 2015. Diurnal vertical migration rhythms and their photoresponse in four phytoflagellates causing harmful algal blooms. Limnol Oceanogr 60: 1251-1264.
• SILVA EDS, ROCHA O & SANTOS-WISNIEWSKI MJD. 2018. Diel vertical migration of Cladocera in a compartment of a tropical reservoir. Acta Limnol Bras 30: e304.
• SIMONCELLI S, THACKERAY SJ & WAIN DJ. 2019. Effect of temperature on zooplankton vertical migration velocity. Hydrobiologia 829: 143-166.
• SOARES MCS, LÜRLING M & HUSZAR VLM. 2010. Responses of the rotifer Brachionus calyciflorus to two tropical toxic cyanobacteria (Cylindrospermopsis raciborskii and Microcystis aeruginosa) in pure and mixed diets with green algae. J Plankton Res 32: 999-1008.
• SPEARS BM, CARVALHO L, PERKINS R, KIRIKA A & PATERSON DM. 2012. Long-term variation and regulation of internal phosphorus loading in Loch Leven. Hydrobiologia 681: 23-33.
• STRICKLAND JDH & PARSONS TRA. 1972. A Practical Handbook of Seawater Analysis, 2ª ed., Ottawa: Fisheries Research Board of Canada: 205 p.
• STRUNECKY O, BOHUNICKÁ M, JOHANSEN JR, CAPKOVÁ K, RAABOVÁ L, DVORÁK P & KOMÁREK J. 2017. A revision of the genus Geitlerinema and a description of the genus Anagnostidinema gen. nov. (Oscillatoriophycidae, Cyanobacteria). Fottea 17: 114-126.
• TAIPALE S, STRANDBERG U, PELTOMAA E, GALLOWAY AW, OJALA A & BRETT MT. 2013. Fatty acid composition as biomarkers of freshwater microalgae: analysis of 37 strains of microalgae in 22 genera and in seven classes. Aquat Microb Ecol 71: 165-178.
• TIBERTI R & BARBIERI M. 2011. Evidences of zooplankton vertical migration in stocked and never-stocked alpine lakes in Gran Paradiso National Park (Italy). Oceanol Hydrobiol St 40: 36.
• TIBERTI R & IACOBUZIO R. 2013. Does the fish presence influence the diurnal vertical distribution of zooplankton in high transparency lakes? Hydrobiologia 709: 27-39.
• TISELIUS P, SAIZ E & KIØRBOE T. 2013. Sensory capabilities and food capture of two small copepods, Paracalanus parvus and Pseudocalanus sp. Limnol Oceanogr 58: 1657–1666.
• UNREIN F, O’FARRELL I, IZAGUIRRE I, SINISTRO R, DOS SANTOS AFONSO M & TELL G. 2010. Phytoplankton response to pH rise in a N-limited floodplain lake: relevance of N2-fixing heterocystous cyanobacteria. Aquat Sci 72:179-90.
• UTERMÖHL H. 1958. Zur vervollkommung der quantitativen phytoplankton - methodik. Mitt Int Verein Limnol 9: 1-38.
• VADADI-FÜLÖP C, SIPKAY C, MÉSZÁROS G & HUFNAGEL L. 2012. Climate change and freshwater zooplankton: what does it boil down to? Aquat Ecol 46: 501-519.
• VAN ZUIDEN TM, CHEN MM, STEFANOFF S, LOPEZ L & SHARMA S. 2016. Projected impacts of climate change on three freshwater fishes and potential novel competitive interactions. Divers Distrib 22: 603-614.
• VISSER PM, VERSPAGEN JM, SANDRINI G, STAL LJ, MATTHIJS HC, DAVIS TW, PAERL HW & HUISMAN J. 2016. How rising CO2 and global warming may stimulate harmful cyanobacterial blooms. Harmful Algae 54: 145-159.
• WETZEL RG & LIKENS GE. 2000. Limological Analyses, 3ªed., New York: Springer Science, 429 p.

Publication Dates

• Publication in this collection
  14 Mar 2022
• Date of issue
  2022

History

• Received
  29 Apr 2020
• Accepted
  21 Aug 2020