2 Siberian Federal University, Krasnoyarsk 660041, Russia
Zooplankton are keystone grazers on primary producers in aquatic ecosystems that transfer energy to higher food web consumers (Sterner, 2009). Consequently, the ability of zooplankton to control the abundance and taxonomic composition of phytoplankton is one of their main ecosystem functions (Ryther and Sanders, 1980; Lampert et al., 1986). The strength of top-down grazing control depends on the abundance and species composition of zooplankton, which, in turns, is affected by various environmental and intrinsic factors (e.g., Frau, 2022).
Based on the intrinsic food traits zooplankton can be divided into functional groups with different abilities to control phytoplankton by their taxonomic composition and grazing type (e.g., Colina et al., 2016) or by the size of grazers (Hulot et al., 2014). For example, Cladocera are considered less selective filter feeders than copepods (Frau, 2022). This means that cladocerans will have a greater impact on the abundance of phytoplankton particles and a less pronounced effect on their taxonomic composition (Colina et al., 2016). Under favourable conditions, they will reduce the abundance of phytoplankton and deter harmful algal blooms in water bodies (Sommer and Sommer, 2006). In addition, the ability to control the abundance of phytoplankton and grazing selectivity vary across zooplankters of different sizes, since the size of the particles consumed by different zooplankters is largely determined by their body size (Burns, 1968).
Grazing can be both size selective and species specific. Size selective grazing affects the size distribution of phytoplankton particles. The species-specific grazing changes the taxonomic composition of phytoplankton. Thus, the top-down effect of zooplankton can change the structure of the phytoplankton. A number of studies showed that by the selective grazing on smaller and palatable algae, zooplankton stimulate the development of toxic cyanobacteria (Mitra and Flynn, 2006; Leitão et al., 2018; Ger et al., 2019) or filamentous algae (Kasprzak and Lathrop, 1997; Hambright et al., 2001), which can affect water quality.
The size and the edibility of phytoplankton, in turn, affect zooplankton. For example, the dominance of large or filamentous algae or colonies in the phytoplankton decreases the filtration efficiency of cladocerans (Webster and Peter, 1978; Mourelatos and Lacroix, 1990). Also, the toxic or low nutritional value phytoplankton can be the reason for hindered growth and reduced abundance of zooplankton grazers (Giani, 1991; Roy et al., 2007).
Among environmental factors, salinity is one of the important factors that control the abundance and taxonomic composition of zooplankton (Schallenberg et al., 2003; Gutierrez et al., 2018). Recently, the salinization of freshwaters has become a global problem (Cunillera-Montcusí et al., 2022). The concentration of dissolved ions in water significantly affects zooplankton. Most often, specific zooplankters are adapted to a certain salinity range, which is determined by their osmoregulation capacities (Aladin, 1991). Such filter feeders as cladocerans, which are able to effectively control the abundance of phytoplankton, generally prefer fresh and moderately saline waters (Gutierrez et al., 2018). A sharp decline in the diversity of zooplankton, including cladocerans, is observed at a salinity of 3 g/L (Remane and Schlieper, 1971; Cognetti and Maltagliati, 2020). When the so-called critical salinity of 8–10 g/L is reached, freshwater species disappear from ecosystems, and they are replaced by brackish water species (Khlebovich, 1969; Williams et al., 1990).
The effect of low and moderate salinities (up to 5 g/L) on the ability of zooplankton to control phytoplankton is of great research interest. Such salinity is still below the threshold for the presence of fish and supports many species of freshwater zooplankton, specifically various cladocerans that considered being effective filter feeders (e.g., Lin et al., 2017). At such salinity, a lake continues to provide ecosystem services typical for freshwater lakes such as support of species diversity or fish provisioning. However, increased salinity can affect zooplankters physiology, grazing rate, and efficiency of filtration (Baillieul et al., 1998; Zadereev et al., 2022b). At the same time, with the salinity increase above 3-g/L small-bodied cladocerans are frequently replaced by large bodied Daphnia, which may change the grazing pressure (Zadereev et al., 2022a). Thus, the interactive effect of salinity on species composition (shift from smaller to larger cladocerans) and individual feeding traits (grazing rate, efficiency of filtration) can affect the ability of zooplankton to control phytoplankton.
In this research, we conducted laboratory experiments to evaluate the intensity and selectivity of grazing of two cladocerans differing in body size in waters from lakes with different salinity. Small and large bodied cladocerans presumably differ in the size range of consumed phytoplankton particles and consequently in an ability to control and shape the phytoplankton. At the same time shift from smaller to large cladocerans is typical for many moderately saline lakes with the salinity increase. The lakes that we carefully selected to sample water for experiments differed not only in salinity but also in taxonomic and size characteristics of phytoplankton. Thus, we were able to test the following assumptions based on the body of knowledge in aquatic ecology: 1) the grazing rate in lower-salinity water will be higher than in higher-salinity water; 2) the grazing rate in water with smaller phytoplankton will be higher than in water with larger phytoplankton; 3) larger cladocerans will have stronger effect on the abundance, size and taxonomic composition of phytoplankton.2 MATERIAL AND METHOD
We used two species of Cladocera that differ in size: the larger Daphnia magna and the smaller Moina macrocopa. Individuals of the D. magna population were collected from the laboratory culture of D. magna that was started several years ago from animals isolated from the pelagic part of Lake Krasnenkoye (54.445 164°N, 90.337 008°E, average long-term salinity 1.0–1.5 g/L (Zadereev et al., 2022a)) located close to study lakes that will be described below. Individuals of M. macrocopa were isolated from the laboratory culture that was established from resting eggs prior to the experiment. Resting eggs were produced by the laboratory culture of M. macrocopa that was established from the resting eggs collected near the Rybinsk reservoir from the freshwater pond by Vladimir Chugunov. Laboratory culture of M. macrocopa is regularly crowded to produce resting eggs, which are used to reestablish the population. Smaller freshwater species of Moina was used as a model small cladoceran. It is not typical for study lakes but this species can be used for suppressing the growth of phytoplankton (Chaichana and Promwang, 2016). Animals of both species were grown in the laboratory under controlled conditions (23 ℃, 16-h light: 8-h dark) at the Institute of Biophysics, Siberian Branch, Russian Academy of Sciences in aged tap water (pH 7.3; total permanent hardness 62.9-mg equivalent of CaCO3 1 per L; total content of cations (macro- and trace elements) 26.4 mg/L) with the unicellular green alga Chlorella vulgaris Beije as a food at concentration above 1×105 cells/mL.
Two days before the experiment, randomly selected animals from both populations were transferred into a culture medium with the non-axenic green alga C. vulgaris at a concentration of 2×105 cells/mL. The culture medium volume was 20 mL per animal.
We used water from Lakes Chalaskol, Sukhoye, Utichye-3, and Dzhirim with natural phytoplankton as the treatment medium in grazing experiments (Table 1). Water from Lake Dzhirim was 50:50 diluted with aged tap water, as 100% mortality of D. magna and M. macrocopa was observed in undiluted water from this lake in preliminary tests. It was necessary to dilute this water as the salinity of water from Lake Dzirim is above critical salinity for freshwater species. It is inhabited by not freshwater but by brackish and saline waters species (Zadereev et al., 2022a). The lake waters used as experimental media were aged at 23 ℃ for 24 h prior to the grazing experiment. The temperature of 23 ℃ was selected as midsummer epilimnetic lake water temperature in this area (Rogozin et al., 2017) to mimic midsummer grazing conditions. The salinity of the lake water was expressed as total dissolved salts (TDS, g/L), calculated automatically by YSI Exo probe (YSI Inc., U.S.A.) based on measurements of the specific electrical conductivity of water.
The grazing experiments were carried out in 50-mL vessels under controlled temperature (23 ℃) in darkness. Two hours before the experiment, animals of each species were placed in 500-mL vessels with the treatment medium, which had been filtered through GF/F filter (Whatman) to remove phytoplankton. After two hours, to start the experiment, the animals (15 adult 2.4-mm body length Daphnia females per 50-mL vessel or 50 adult 1.3-mm body length Moina females per 50-mL vessel) were placed into the treatment vessels. The control 50-mL vessels were kept without animals. With each treatment media, we had 3 control vessels without animals and 3 vessels with animals. Experiments with Daphnia and Moina were run sequentially.
After 5 h of grazing, water was removed from the control and treatment vessels, and the size and fluorescence characteristics of phytoplankton were measured. The body length (L, mm) of animals from each vessel was measured at 16× magnification. The dry weight (DW, μg) was calculated as DW=L2.917× 0.094 for Daphnia and DW=L3.027×0.114 for Moina (Gutelmaher, 1986; Gutelmaher et al., 1988).
To determine the size and number of phytoplankton particles in the medium from control and treatment vessels, we used a FlowCam flow cytometer (FluidImaging Inc., USA) with a 50-μm capillary. From each vessel, we took a 0.2-mL sample and ran it through the capillary at a flow rate of 0.04 mL/min. The measurements were carried out in the trigger mode. In the trigger mode, the FlowCam captures images of particles when the fluorescent signal exceeds the threshold (400 nm) at least in one of the detection channels. The FlowCam was configured with an excitation 532 nm laser and two photodetectors detecting red (wavelength≥650 nm, chlorophyll a (chl a)) and orange (575±30-nm wavelength, phycoerythrin) fluorescence. The minimal size of captured images of particles was set at 2 µm. With the FlowCam, we analyzed the abundance and volume of particles, the size distribution of particles of different diameters, and the area based average diameter, the average particle volume, and the average aspect ratio of particles (Zadereev et al., 2021).
To determine the fluorescence characteristics of phytoplankton in the medium from control and treatment vessels, we used a FluoroProbe multichannel fluorimeter (bbe Moldaenke GmbH, Germany). The total concentration of chlorophyll and the proportions of green algae, diatom algae, and cyanobacteria in the phytoplankton were estimated; they were automatically determined by the spectrofluorimeter based on fluorescence signals and the manufacturer calibrations. The measurements were carried out in a 30-mL quartz cuvette. Each measurement was carried out for 60 s. Within that time interval, up to 15 values of fluorescence signals were recorded. The average of those values was used (Zadereev et al., 2021).
To estimate the specific grazing rates (SGR) expressed in chl a or in the number of particles consumed per dry weight (DW) of animals per day, first, we calculated the average chl-a content (Fcontrol, μg chl a, FluoroProbe measurements) or the number of particles (Fcontrol, particles, FlowCam measurements) in control vessels. Then, for each treatment vessel, we subtracted the corresponding value in a treatment vessel (Ftreatment) from the control value. This difference demonstrates the decline in the chl-a content or in the number of particles in treatment vessels after 5 h of grazing. The resulting value was divided by the dry weight of animals in the treatment vessel (DW, μg) and converted to daily grazing (as the duration of grazing experiments was 5 h, the value was first divided by 5 and after multiplied by 24). Finally, the average for 3 treatment vessels was calculated to obtain specific grazing rates:
We used multiple linear regression to estimate the effect of independent variables on the specific grazing rates of Daphnia and Moina. As independent variables at the first stage, we selected salinity, the concentration of chl a, proportions of different algae in phytoplankton (green algae (green_pct), cyanobacteria (cyan_ pct) and diatoms (diat_ pct)), the abundance of particles (particle_count), particle diameter (diam), and aspect ratio (AR). We calculated the correlation matrix for these variables and estimated the collinearity (Table 2).
To reduce collinearity, we excluded collinear variables with the regression coefficient R > 0.7 from further analysis. In each pair of variables, we excluded one variable based on ecological reasoning (Dormann et al., 2013). The final set of independent variables included salinity, the concentration of chl a, and the proportion of green algae in phytoplankton. Salinity and total chl-a content are basic predicting variables. Salinity determines the water density and affects the physiology of animals including filtration rate. Total chl-a content determines the amount of available food. The proportion of green algae in phytoplankton determines the quality of phytoplankton and related size characteristics of phytoplankton particles. The lower proportion of green algae corresponds to the higher taxonomic diversity and the higher proportions of diatoms and cyanobacteria. The multiple linear regression was implemented using the R statistical programming language (R Core Team, 2021).
In addition, we analyzed with two-way ANOVA our results as an experiment of nested design with two factors: water type (four lakes) and zooplankton species (two species). Changes in the average particle diameter and proportions of green algae in the control and treatment vessels after grazing of animals were also assessed with two-way ANOVA (factors: medium (4 types of lake water with different salinity and phytoplankton composition) and impact (3 types of impacts: control and two Cladocera species)).
We calculated the average proportions of particles of different diameters (7 diameter classes in the range of diameters from 0 to 42 μm at 6-μm increments) in control vessels. Then, the difference between the proportions of particles of each diameter class in the treatment and control vessels was determined: negative values characterized a decrease in the proportion of particles of a diameter class after grazing, positive values—an increase in the proportion of particles of a diameter class after grazing. Factorial analysis of variance (factors: diameter (7 size classes), treatment medium (4 different media), and species (2 species)) was used to estimate the effect of grazing on the size distribution of particles.
All statistical calculations were performed in STATISTICA 7.0.3 RESULT 3.1 Characteristics of phytoplankton in treatment medium
We selected lakes to sample water for grazing experiments using two criteria: 1) salinity of water should be different; 2) the phytoplankton should be different in both abundance and taxonomic composition (see Supplementary Fig.S1 for typical images of phytoplankton particles from study lakes). The least abundant phytoplankton based on chl-a measurements, which was dominated by cyanobacteria (71%), was present in water with the lowest TDS value from Lake Chalaskol. In contrast, the water from Lake Sukhoye, whose salinity was almost twice higher, had the greatest abundance of phytoplankton with a 60:40 ratio of green algae to cyanobacteria. Waters from Lakes Utichye-3 and Dzhirim, which were similar in salinity but different in ionic composition, had comparable abundances of phytoplankton. However, the composition of phytoplankton from Lake Utichye-3 was 60:40 diatoms to green algae. At the same time, 94% of phytoplankton in Lake Dzhirim was identified, using fluorescent signals, as green algae (Tables 1 & 3).
The abundance of particles measured with FlowCam strongly correlated with the total chl-a concentration (correlation analysis, R=0.98, P=0.006). The average diameter of phytoplankton particles differed between lakes (ANOVA, F(3, 20)=156.61, P < 0.001) (Table 3). The largest average diameter of particles was observed in Lake Chalaskol followed by particles in Lakes Utichye-3 and Dzhirim, which were smaller by a factor of almost two, and even smaller particles in Lake Sukhoye. The aspect ratio of particles was also different between lakes (ANOVA, F(3, 20) =259.3, P < 0.001). The highest aspect ratio of particles was observed in Lake Utichye-3 and the smallest in Lake Dzhirim.3.2 Grazing experiment 3.2.1 Grazing rate
The multiple linear regression predicted the variability of the specific grazing rates of both study species (Table 4). However, this high explanatory power was mostly related to the limited number of observations (water from 4 lakes) and correlations between variables. Still, the model can be used to estimate the relative importance of selected variables. The most important variable was the proportion of green algae in the phytoplankton, which in this study is broadly related to the quality of phytoplankton (proportions of other taxa, size of phytoplankton particles) (Table 2). The specific grazing rate of cladocerans increased with the decline in the proportion of green algae in phytoplankton and the increase in the proportions of other phytoplankton taxa, specifically diatoms, which were selectively consumed. The specific grazing rate was positively affected by the amount of total chl a in the medium. Salinity also positively affected the specific grazing rate, while this effect was most probably associated with the effect of the proportion of cyanobacteria and size of phytoplankton particles, which was negatively correlated with salinity (Table 2).
The effect of study variables on the specific grazing rate is more evident when presented for separate lakes (Figs. 1–2). The effect of water type (four lakes) and combined effect of water type and zooplankton species (two species) on the specific grazing rate expressed in µg of chl a were significant (two factorial ANOVA, P < 0.001). The highest grazing rate in µg of chl a was observed in waters from Lakes Sukhoe and Utichye-3 followed by lower grazing rates in waters from Lakes Chalaskol and Dzhirim. The grazing rate of Daphnia was higher that grazing rate of Moina in water from Lake Sukhoe and the grazing rate of Moina was higher that grazing rate of Daphnia in water from Lake Utichye-3 (Fig. 1).
The effect of water type (four lakes) on the specific grazing rate expressed in number of particles was also significant (two factorial ANOVA, P < 0.001). The highest grazing rate in the number of particles was observed in water from Lake Sukhoe, followed by the value in water from Lake Utichye-3, which was smaller by a factor of almost two, and very low values in waters from Lakes Chalaskol and Dzhirim (Fig. 2).3.2.2 Grazing selectivity
We observed taxon-specific feeding of both species in Lake Utichye-3, where after 5 h of grazing the diatom algae were completely consumed and, consequently, the proportion of green algae increased, and for Daphnia in Lake Dzhirim, where after grazing the proportion of green algae slightly decreased (Fig. 3).
The size selective feeding was also detected, as we observed grazing related changes in the average diameter of phytoplankton particles (Fig. 4). The most pronounced effect was observed in the water from Lake Chaloskol, where the average particle diameter was increased after Daphnia grazing and decreased after Moina grazing.
Factorial ANOVA demonstrated significant changes in the proportions of phytoplankton particles of different sizes after grazing related to the effect of particle diameter and joint effects of particle diameter+ treatment medium, particle diameter+species and particle diameter+treatment medium+species (Table 5).
The effect of the particle diameter was significant, as the pronounced decline after grazing was observed in the proportion of phytoplankton particles in the 6– 12-µm size range. By contrast, the proportions of smaller particles (2–6 µm) and some of the larger particles (24–30 µm) increased. The difference in selective feeding between species and treatment media was also detected. In waters from Lakes Chalaskol and Utichye-3 after Daphnia and Moina grazing, the size distribution of particles was changed differently; in water from Lake Sukhoye, it remained almost unchanged; in water from Lake Dzhirim, it changed similarly (Fig. 5).4 DISCUSSION
The observed variability across specific grazing rates of study species in waters from different lakes is explained by the effects of the abundance of food, salinity of the water, and size and taxonomic characteristics of phytoplankton. The highest grazing rates were observed in waters with low and medium salinity and high abundance of small phytoplankton particles. It is generally accepted that grazing rate depends on the abundance of food particles (Rigler, 1961). Thus, the higher the abundance of edible phytoplankton, the higher is the grazing rate. Much lower grazing rates were observed in the water with the highest salinity in spite of the high abundance of food and in lower-salinity water with the largest phytoplankton. Previously, we demonstrated that the ingestion rate of D. magna is reduced at salinity above 4 g/L (Zadereev et al., 2022b). Also, the grazing rate of zooplankton was observed to be low with less edible phytoplankton (Vad et al., 2020). Thus, our study confirmed that both the salinity of water and the size of phytoplankton could suppress the grazing rate of freshwater filter feeders.
We also observed some differences between smaller and larger cladocerans in their grazing rates. The smaller Moina had a higher grazing rate in moderately saline water from Lake Utichye-3, with a medium abundance of relatively large green and diatom algae. The larger Daphnia had a higher grazing rate in the lower-salinity water from Lake Sukhoye, with a high abundance of small particles. As both of the tested species are able to live and reproduce at the salinities of these waters (1.7 g/L in Lake Sukhoye and 4.8 g/L in Utichye-3) (Lopatina et al., 2021; Zadereev et al., 2022a), we assume that this difference is explained by the phytoplankton abundance and size distribution. Other studies demonstrated that Daphnia magna is an efficient filter feeder on particles of a wide size range including smaller particles (Brendelberger, 1991). Also, the filtration rate of the large bodied Daphnia is higher than that of the smaller Moina. That is why, in the medium with the highest food abundance, they demonstrate the highest grazing rate.
We detected size-selective grazing of both species, as they predominantly consumed phytoplankton particles in the 6–12-µm size range. This size-selective grazing was observed in waters of all salinities. Only in one treatment medium did we observe a remarkable increase in the proportion of large phytoplankton particles due to the effect of selective grazing of the large Daphnia. This effect was manifested in the lake water with the initially high proportion of large phytoplankton. Even though the grazing rate in this medium was low, the selective grazing of Daphnia on small particles increased the average size of particles and the proportion of large particles. This result supports some previous findings, which suggested that due to size-selective grazing, the size distribution of phytoplankton can be significantly changed and shifted to phytoplankton resistant to filter feeders (Sommer et al., 2001).
In most other cases, selective grazing resulted in an increase in the proportion of smaller particles. There are two explanations of such an increase in the proportion of small particles. First, the number and, consequently, the proportion of small particles can be related to the interactions of animals with phytoplankton and between each other and defragmentation of larger phytoplankton particles. As we placed a relatively large number of animals per treatment vessel to detect the grazing effect within a short time (5 h), we could expect a large number of animal-animal and animal-phytoplankton particles encounters and possible increase in the number of small particles. Second, with a 50-µm flow cell, the accuracy of counts of particles drops with an increase in the diameter of particles. At the same time, for small particles with a diameter close to the minimal resolution of the FlowCam (2 µm), the accuracy of identification will be minimal. Thus, the relatively large number of small unidentifiable particles will be imaged and counted by FlowCam. The possible solutions are 1) to use a combination of flow cells with different diameters (e.g., 50 and 100 µm) to increase the accuracy of counts for larger particles and 2) to increase the minimum size of analyzed particles from 2 µm to ca. 3– 4 µm. At the same time, small phytoplankton particles, even below 2 µm, are known to be part of the grazing diet of cladocerans (Geller and Müller, 1981; Brendelberger, 1991). Therefore, the accurate account of very small particles in the grazing experiments is still a challenging task.
We also detected taxon-specific feeding, as both species consumed almost all diatoms, which increased the proportion of green algae in the medium. At the same time, the remaining green algae were also in the edible size range. So, this decline in the diatoms is most probably not related to selective feeding based on the size properties of particles of different algae taxa. It is well known that diatom algae are often highly preferable food for zooplankton (Gulati and Demott, 1997). We did not detect taxon-specific feeding in waters from other lakes, where the phytoplankton comprised green algae and cyanobacteria. That was not surprising, as cyanobacteria per se are not inedible food (Tillmanns et al., 2008). Indeed, many species of cyanobacteria can be of low food quality because of their shape (e.g., filaments) or toxicity (Wilson et al., 2006). However, cyanobacteria in the edible size range will be effectively consumed by zooplankton (Work and Havens, 2003).
We can summarize that in our experiments, grazing of two Cladocera species was mostly determined by the abundance of phytoplankton, as we observed higher grazing rates at higher abundances. This effect was modified by salinity, as at salinity close to critical (ca. 3 g/L of NaCl), the grazing rate was reduced. The amount of NaCl was a better predictor of the salinity effect on grazing rate than total dissolved salts. Water from Lake Utichye-3 and diluted water from Lake Dzhirim contained almost equal amounts of total dissolved salts but differed considerably in NaCl content. Indeed, the ionic composition of saline water varies between lakes. The effects of different ions on the osmoregulation system and physiology of animals are not comparable (Kaushal et al., 2019). Some ions are more toxic and/or stressful to organisms. Thus, it is very important when discussing the unfavorable effects of salinity to refer not to the total amount of dissolved salts but to the composition of this chemical cocktail (Cunillera-Montcusí et al., 2022).
The grazing rate was also reduced in the medium with larger phytoplankton. There were no remarkable differences between smaller and larger grazers, while larger grazers demonstrated higher grazing rates at higher abundances of phytoplankton and a stronger ability to shift the size distribution of phytoplankton particles. Thus, we support the observation that large cladocerans frequently suppress phytoplankton (Matveev and Matveeva, 1997). However, this ability will be determined by the size and taxonomic characteristics of phytoplankton.
We performed our experiments in the ecologically relevant range of salinities, as with the further salinity increase, the freshwater species of zooplankton will be replaced by salinity tolerant species (Zadereev et al., 2022a). Results demonstrate that the grazing pressure on phytoplankton in this salinity range can be reduced with the salinity increase due to reduced grazing. Also, size-selective and taxon-specific feeding may result in a fewer edible phytoplankton community. As we used natural phytoplankton and waters from different lakes, other factors can affect the grazing rate and the consequent ability of the zooplankton to control phytoplankton (Frau, 2022). Natural phytoplankton community composition and size structure is a result of long- and short-term ecosystem interactions, nutrient availability, etc. Moreover, phytoplankton of similar size can have different food quality. As we performed short-term experiments, we estimated only the potential ability of cladocerans to control phytoplankton. This estimate can be supplemented by the longer mesocosm or enclosure experiments (Póda and Jordán, 2020). Such experiments can follow the dynamics of phytoplankton and zooplankton, which vary in size and species, in waters of different salinity. The ultimate goal is to understand the complex interactions of phytoplankton and zooplankton and to develop biological methods of top-down control of phytoplankton.5 CONCLUSION
We studied the grazing rate and feeding selectivity of two Cladocera species of different body size in waters from lakes with different salinity and different species and size composition of phytoplankton. We confirmed that high salinity can reduce the grazing rate and the effect of the larger cladoceran on the size and taxonomic composition of phytoplankton is stronger. However, effects of the abundance and size and species composition of phytoplankton on the specific grazing rate were also strong as for both species the grazing rate increased with the increase in the abundance of food, in water with smaller food particles it was higher than in water with larger food and it was both size-selective and taxon-specific.6 DATA AVAILABILITY STATEMENT
All data generated or analyzed during this study are included in this published article or available from the corresponding author on reasonable request.7 ACKNOWLEDGMENT
We are grateful to professional English translator and editor Elena Krasova for linguistic improvements. Two anonymous reviewers are highly acknowledged for valuable comments and discussions that improved this MS and will stimulate further research. The research was supported by the State Assignment of the Ministry of Science and Higher Education of the RF (No. 0287-2021-0019).
Electronic supplementary material
Supplementary material (Supplementary Fig.S1) is available in the online version of this article at https://doi.org/10.1007/s00343-022-2158-2.
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