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Date Submitted: 19 June 2007; Date Accepted in Principle: 8 October 2007; Date Accepted for Publication: 6 November 2007

Interactions between phytoplankton and zooplankton in the hypertrophic

Swarzędzkie Lake in western Poland

Ryszard Gołdyn, Katarzyna Kowalczewska-Madura

Department of Water Protection, Adam Mickiewicz University, Poznań

Umultowska 89, 61-614 Poznań, Poland; e-mail: [email protected]

Key words: phytoplankton, zooplankton, filtering rate, grazing rate, water bloom, Swarzedzkie

Lake

Communicating Editor: K. J. Flynn

© The Author 2007. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: [email protected]

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Abstract

The hypertrophic Swarzędzkie Lake, Poland, is characterised by high species diversity, abundance

and biomass of both phytoplankton and zooplankton (up to 99.5 mgWW L-1 and 817.75 µgDW L-1,

respectively). The community grazing rate calculated with the use of two empirical models, and

based on herbivorous crustaceans, peaked in spring and early autumn up to 150.6 % of water

filtered per day, and was the lowest during winter. Simple statistics revealed a positive correlation

between zooplankton biomass and chlorophyll a concentration (r = 0.404, p = 0.033) and between

zooplankton abundance and phytoplankton biomass (r = 0.42, p = 0.028). Canonical statistics

indicated, however, that the relationship exists only with size groups and/or living forms of a few

taxonomical groups of phytoplankton. Redundancy analysis (RDA) confirmed a positive influence

of the community grazing rate on micro- and nanoplanktonic Cryptophyceae, but not on the

microplanktonic Cyanobacteria, as was suggested by canonical correlation analysis. RDA also

indicated a weak negative influence on nanoplanktonic Euglenophyceae and Chlorophyceae exerted

by filtering crustaceans. Some taxonomically diverse flagellated nanoplanktonic algae were grazing

sensitive, whereas microplanktonic cryptophytes and coenobial green algae were significantly

grazing resistant.

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Introduction

Strong relationships exist between phytoplankton and zooplankton. For instance the main

systematic groups of zooplankton include many taxa, which feed on phytoplankton. Selective

grazing by zooplankton is an important factor affecting the structure of phytoplankton communities.

However, phytoplankton structure also influences the taxonomic composition and dominance of the

zooplankton. These animal components are mainly filtrators, sedimentators, or raptorial predators

(Karabin, 1985). Among them, filtrators usually exert the strongest effect on phytoplankton

abundance in lakes. In Daphnia, the filtering rate is positively correlated with animal size, water

temperature, and phosphorus concentration in the seston (Darchambeau and Thys, 2005). Grazing

by cladocerans creates a selective pressure on the phytoplankton community, causing elimination of

organisms that do not exceed a precisely defined size (Gliwicz, 1980). As a result inedible large-

sized algae dominate phytoplankton communities (Kawecka and Eloranta, 1994).

In many cases predatory copepods exert a strong influence on the phytoplankton composition.

The copepods suppress large phytoplankton, while nanoplanktonic algae increase in abundance

(Sommer et al., 2003). The algal species that are resistant to grazing and predation are more likely

to survive, but also can make filter feeding more difficult. Daphnia species are particularly sensitive

to disturbances of the filtering mechanism caused by large algae (Dawidowicz, 1990). Because of

the constant feeding pressure of zooplankton on phytoplankton, the more resistant algae may

become more and more abundant during the growing season. This, in combination with the pressure

exerted by fish on large-sized zooplankton, results in the restructuring of the community of

zooplankton towards the dominance of small-sized organisms resistant to disturbances and trophic

interactions (Gulati, 1990; Meijer, 2000; Kozak and Gołdyn, 2004).

This study in Swarzędzkie Lake, Poland, describes the interactions between these two groups of

planktonic organisms, focusing on the seasonal quantitative and qualitative composition of phyto-

and zooplankton. We hypothesised that filter-feeding zooplankton will suppress small edible

phytoplankton species, thereby decreasing their abundance. Simultaneously, nutrients excreted by

zooplankton will stimulate the growth of large, grazing resistant species. Grazing also diminishes

the per capita resource competition of phytoplankton. Predation of copepods on larger species of

phytoplankton will favour gelatinous colonial species of cyanobacteria and green algae thus causing

an increase in their abundance, as observed in enclosure experiments by Sommer et al. (2003).

Method

Swarzędzkie Lake is a shallow lake of glacial origin. It is located in the north-western part of

the town of Swarzędz, at the border of the city of Poznań in western Poland (52º25’N, 17º04’E).

Morphometric data for this lake are presented in Table I.

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The Cybina River (total length 41 km) flows through the lake and is a tributary of the Warta

River. The catchment of the Cybina covers 195.5 km2 and is dominated by farmland (77%). The

lake is also supplied by the stream Mielcuch which has been polluted by storm water over-flows

from the town of Swarzędz. The north-eastern part of the lake is wider and deeper than the south-

western section which is ca. 2 m deep (Szyper et al., 1994) (Table I). The lake is enriched with

nutrients from the catchment and from the bottom sediments (Gołdyn and Kowalczewska-Madura,

2005; Kowalczewska-Madura, 2003). In summer the lake is characterised by oxygen depletion in

the deeper layers of water and by high concentrations of total phosphorus and total nitrogen,

reaching up to 1.55 mgP·L-1 (50 µM·L-1) and 13.4 mgN·L-1 (957 µM·L-1). Its present trophic state

has been classified as advanced eutrophic, or even hypertrophic (Kowalczewska-Madura, 2005).

Research on the composition, abundance and biomass of phyto- and zooplankton in

Swarzędzkie Lake was conducted monthly from June 2000 to September 2002. The sampling

station was located in the central, deepest point of north-eastern part of the lake. Water samples for

phytoplankton analysis were taken just below the surface. Samples for analyses of chlorophyll a

and zooplankton were collected using a 5-L Limnos water sampler every 1m in a vertical profile.

For zooplankton counting – 10 L of lake water was filtered through a plankton net (mesh size 40

µm). Samples of phyto- and zooplankton were preserved with acid Lugol’s solution (Wetzel and

Likens, 2000). Chlorophyll a was assessed with the Lorenzen method after extraction in acetone

and corrected for pheopigments a (Wetzel and Likens, 2000). Phytoplankton counting was made in

5-mL settling chambers following a settling period of 24 h, then examined with an inverted

microscope (magnification 400×). Number of specimens in 1ml was counted, assuming as 1

specimen was the cell, coenobium or filament, in dependence on the manner of occurrence. It was

assumed for filaments 100 µm as the standard length, for coenobia – the most frequent cell number

and for large spherical colonies – 100 cells as the standard specimen.

The biovolume of each species was estimated by applying closest geometric formulae following

Hindak (Hindak, 1978) and Wetzel and Likens (Wetzel and Likens, 2000). All species were divided

on two size groups: nanoplankton (below 30 µm) and microplankton (over 30 µm). Analyses of

zooplankton were carried out in Sedgwick-Rafter chambers of 1 mL volume, under a microscope

magnification 100−200×. Zooplankton biomass was calculated following Bottrell et al. (Bottrell et

al., 1976). For the calculation of phyto- and zooplankton biomass ca. 30 specimens of all prevailing

species were measured. Other species were measured occasionally or mean literature data were

used. As the differences among zooplankton data in vertical profile were not statistically significant,

mean values were calculated and generally taken into account. The biomass of phytoplankton was

expressed as wet weight (WW) in mg L-1, and of zooplankton as dry weight (DW) in µg L-1.

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The grazing rate of large filter feeders, including Cladocera (excluding Leptodora kindtii) and

Calanoida, was calculated by two models. The first model, proposed by Knoechel and Holtby

(Knoechel and Holtby, 1986), is based upon 2 parameters of zooplankton community: body length

and abundance of each taxon:

403.2

1396.7 LiaF

n

ii∑

=

=

γ = 0.1 F

where: F = filtering rate (mL·L-1·d-1); Li = average body length of the animal (mm); α = abundance

of the animal taxon (numbers·L-1); n = number of animal taxa; γ = grazing rate (% d-1); 0.1 –

coefficient for recalculation of units from mL·L-1·d-1 into %·d-1.

The second model, proposed by Lampert (Lampert, 1988), estimated the grazing rate by a

function of zooplankton biomass feeding on phytoplankton:

γ = 4.5 + 231W,

where: W = zooplankton biomass (mgDW L-1). Application of those two models allowed the

comparison of results based on different parameters characterising zooplankton.

For general relationships between zooplankton and phytoplankton variables we used simple or

multiple regression (STATISTICA version 7.1). When more variables from each side (response and

predictor variables) should be taken into account, we used canonical correlation analyses

(STATISTICA 7.1). Total redundancy indexes, which were calculated in these analyses, were used

to estimate how much of the actual variability in one set of variables was explained by the other. All

analysed data were converted to normal distribution. They were also examined to detect possible

outliers. As the data of phytoplankton and zooplankton were temperature dependent, they create

time-dependent series. To eliminate the influence of temperature, its data were used as a covariable

in redundancy analyses (RDA) (CANOCO 4.5). Because the gradient lengths of explanatory

variables were short, the RDA was selected over canonical correspondence analysis (CCA) as

suggested by ter Braak and Šmilauer (ter Braak and Šmilauer, 2002).

Results

During this study, 296 taxa of cyanobacteria and eukariotic algae of 9 systematic groups were

identified in Swarzędzkie Lake. Total phytoplankton abundance varied seasonally from 6,970

specimens mL-1 (February 2002) to 61,300 specimens mL-1 (May 2001) (Fig. 1). Higher values

were recorded in spring and summer, and lower in winter. Differences in abundance were also

observed between years. The mean number of organisms between June and September was 23,700

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specimens mL-1 in 2000 and 27,080 specimens mL-1 in 2001, but in 2002 it decreased to

9,730 specimens mL-1. In terms of number of specimens, cyanobacteria prevailed, accounting on

average for 37.6% of total phytoplankton abundance. The number of cyanobacteria in summer 2001

reached ca. 40,000 specimens mL-1. The most numerous were Pseudanabaena limnetica (Lemm.)

Kom., Planktothrix agardhii (Gom.) Anagn. et Kom., and Limnothrix redekei (van Goor) Meffert.

Apart from cyanobacteria, Chlorophyceae, Bacillariophyceae and Cryptophyceae reached relatively

high numbers (Fig. 1).

Calculated biomass ranged from 5.68 mgWW L-1 (February 2002) to 99.5 mgWW L-1 (August

2002) (Fig. 2). Cryptophytes accounted for the highest mean contribution (25.7%) to phytoplankton

biomass. The dominant species in terms of biomass were Cryptomonas reflexa Skuja and C.

curvata Ehr. emend. Penard. Their biomass reached up to 16.97 mgWW L-1. Also diatoms and

green algae were important contributors to total biomass. A marked increase in phytoplankton

biomass was recorded in August 2002. This was due mainly to dinoflagellates, especially the

dominant Ceratium hirundinella f. furcoides Levander (48.4 mgWW L-1) and C. hirundinella f.

austriacum (Zed.) Bachm. (31.6 mgWW L-1).

Division of phytoplankton biomass between nano- (< 30 µm) and microplankton (> 30 µm)

revealed a distinct prevalence of microplankton over nanoplankton during spring and summer

periods, particularly in 2001 and 2002 (Fig. 3). Greater values of nanoplankton were observed twice

a year – in early spring (March) and late summer (August-September) (Fig. 3).

Chlorophyll a concentration indicated seasonal fluctuations (Fig. 3 and 6) similar to those of

phytoplankton biomass. Its value decreased with the increasing depth of the vertical profile of the

lake. The highest values were usually recorded at the surface or at the depth of 1 m. The maximum

value was 109.7 µgChla·L-1 (Aug’02, depth 1 m), and the minimum was 0.8 µgChla·L-1 (Jan’01,

depth 4 m).

The zooplankton community was composed of 96 taxa, including 67 rotifers, 17 cladocerans,

and 12 copepods. Juvenile stages of copepods (nauplii, copepodids) were considered jointly.

Zooplankton abundance ranged from 7 ind.L-1 (February 2001) to 19,400 ind.L-1 (June 2000), and

peaked in spring or summer. In terms of abundance, zooplankton was dominated by rotifers, which

reached ca. 18,000 ind.L-1 and accounted on average for 86.6% of total abundance. The dominant

rotifer taxa were: Keratella cochlearis cochlearis Gosse, K. cochlearis tecta (Gosse), K. quadrata

Müller, Pompholyx sulcata Hudson, Synchaeta oblonga Ehrenberg and S. pectinata Ehrenberg (Fig.

4a). Cladoceran numbers varied from 1 ind.L-1 (February 2001) to 721 ind.L-1 (June 2000). The

most abundant among them were Daphnia cucullata Sars, Bosmina coregoni Baird, B. longirostris

(O.F. Müller) and Chydorus sphaericus O.F. Müller (Fig. 4b). Among the copepods, juvenile stages

were the most numerous, accounting on average for 87.9% of all organisms of this group (Fig. 4c).

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Zooplankton biomass ranged from 0.108 µgDW L-1 (February 2001) to 817.75 µgDW L-1 (June

2000), and was the highest between spring and autumn (Fig. 5). Copepods accounted on average for

53.4% of zooplankton biomass. Apart from the dominant juvenile forms (46% of total zooplankton

biomass), the highest biomass was represented by Mesocyclops leuckarti (Claus), Cyclops vicinus

Uljanin, Thermocyclops oithonoides (Sars) and Eudiaptomus gracilis G.O. Sars. Among

cladocerans, the most important biomass contributors were Daphnia cucullata and Leptodora kindti

Flacke. The biomass of rotifers varied from 0.06 to 286.2 µg L-1 (Fig. 5), with this group having the

smallest mean contribution to the total zooplankton biomass (11.5%). Among rotifers, the highest

biomass was by Pompholyx sulcata, Keratella quadrata (Müller), Polyarthra dolichoptera Idelson

and Asplanchna priodonta Gosse.

The community grazing rate, according to the model of Knoechel and Holtby, was the highest in

spring and early autumn and very low in winter (Fig. 6). The maximum value, much higher than in

any other month of the study, was recorded in May 2002, when it was to 150.6 % d-1 (Fig. 6). A

comparison of the annual summer means (June−September) show that 2002 was characterised by

the highest grazing rate, when the summer mean was 38.6 % d-1. In 2000 the corresponding value

was 30.4 % d-1, whereas in 2001 it was only 21.3 % d-1.

In the vertical profile, calculated grazing rates were highest at 2 m and the lowest near the

bottom, i.e. at the depth of 5 or 6 m, however, the differences were not statistically significant. The

maximum (351.9 % d-1) was recorded in May 2002. The highest specific grazing rates were by

Daphnia cucullata − up to 142 % d-1 (May 2002). This was the largest cladoceran filter-feeding

species in Swarzędzkie Lake and was most dominant during the warm seasons. In colder periods,

the highest grazing rates were recorded for Eudiaptomus gracilis − up to 5.4 % d-1 (November

2000).

The community grazing rate, calculated according to Lampert’s model (Lampert, 1988), showed

a similar seasonal variation (Fig. 6), and its mean values for the vertical profile ranged from 0

(December 2001) to 87.56 % d-1 (May 2002). Also its vertical variation was similar to that observed

using the Knoechel and Holtby’s model. In both models the same zooplankton species were the

most efficient filter feeders. The only difference in these methods is the much larger range of results

obtained from Knoechel and Holtby’s model.

The general relationship between phytoplankton and zooplankton biomass a significant (but not

strong) linear correlation between zooplankton biomass and chlorophyll a concentration both in the

water layer just below the surface and between mean values from the vertical profile (r = 0.404,

p = 0.033 and r = 0.608, p = 0.0006, respectively). Moreover, exponential correlations were found

between zooplankton abundance and phytoplankton biomass calculated from biovolumes (r = 0.42,

p = 0.028) and between zooplankton abundance and chlorophyll a concentration (r = 0.57,

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p = 0.001) just below the surface. Also, the grazing rate calculated by the model of Knoechel and

Holtby (K&H) was positively correlated with chlorophyll a in the vertical profile (r = 0.580,

p = 0.002). Multivariate regression analyses between grazing rate (K&H) and the two size classes of

phytoplankton biomass showed a positive relationship with the microplanktonic biomass (r = 0.488,

p = 0.009), however with the nanoplankton was not statistically significant (r = 0.133, p = 0.77).

The canonical correlation analysis comparing the zooplankton variables (grazing rate, rotifers

and copepods biomass – left set Table III) versus two size groups of phytoplankton (nano- and

microplankton – right set Table III) indicated a similar relationship. As many as 26% of

phytoplankton variance was explained by the zooplankton variables (Table II). Canonical factor

loadings suggested that grazing rate and rotifers were associated with a positive influence on the

microphytoplanktonic biomass, while copepods – negative one (Table III). Canonical weights,

however, indicated a negligible role of Rotifera in this process. Canonical weights explain unique

contributions of the respective variables with a particular weighted sum or canonical variate, so they

are more important than factor loadings, which only overall correlation of the respective variables

with the canonical variate. The influence of zooplankton variables on nanophytoplanktonic biomass

was positive, but very weak. Taking into account 14 groups of phytoplankton instead of 2 size

groups, it was indicated that a single zooplankton variable explained only 6-7% of phytoplankton

variance. Grazing rate together with rotifer and copepod biomass explained about more twice the

variance (16.5%) and it was a little more than the influence of temperature exerted on

phytoplankton (Table II). The positive influence of zooplankton on phytoplankton variables

indicated above, was not identical with the results of canonical analysis using 14 phytoplankton

groups. Canonical factor loadings testified that this positive influence on microplankton was exerted

mainly on Cryptophyceae, less on Conjugatophyceae and Cyanobacteria. However, this influence

was distinctly negative on nanoplanktonic Euglenophyceae and Chrysophyceae and also positive on

nanoplanktonic Cryptophyceae, Cyanobacteria and Chlorophyceae (Table IV). Canonical weights

of phytoplankton groups mentioned above were also the largest, showing their important

contribution to the right canonical variable.

The RDA analyses confirmed the distinct positive influence of grazing rate on large and small

cryptophytes. It was visible mainly in winter, but less in autumn and spring (Fig. 7). It was not

indicated, however, for Cyanobacteria where there is a distinct negative influence, suggesting a

possible grazing of filtrators on Cyanobacteria that occurred mainly in summer. A lesser negative

influence of grazing rate was indicated for the microplanktonic chlorophytes, diatoms and

euglenophytes. A similar low negative influence was with the nanoplanktonic chlorophytes algae

and euglenophytes during autumn and winter (Fig. 7). Rotifers exerted mainly a negative influence

on nanoplanktonic Cyanobacteria and Chrysophyceae and partly on microplanktonic Dinophyceae

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and Cyanobacteria. Similar, though weaker influence was exerted by Copepoda. This group exerted

also positive, though rather weak effect on microplanktonic Conjugatophyceae, nanoplanktonic

Bacillariophyceae and Euglenophyceae (Fig. 7).

The above analyses were generally confirmed by simple regression analyses between the

grazing rate and particular phytoplankton species. These analyses identified the grazing sensitive

species (negative correlation) and grazing resistant species (positive correlation). Small,

taxonomically diverse flagellated species belong to the first group: Chrysococcus skujae Heyning,

Ch. triporus Matvienko, Ochromonas mutabilis Klebs, Chlamydomonas sp., Scourphieldia

cordiformis Takeda. Larger cryptophytes and mostly coenobial green algae belong to the second

group: Cryptomonas curvata, C. reflexa, C. marssonii Skuja, Lagerheimia genevensis (Chod.)

Chod., Scenedesmus acuminatus (Lagerh.) Chodat, Selenastrum capricornutum Printz, Tetrastrum

triangulare (Chod.) Kom.

Reversal of the RDA analysis made possible the evaluation of phytoplankton influence on the

zooplankton biomass. Microplanktonic Cyanobacteria and Cryptophyceae positively influenced

Cladocera, but not in summer months. Instead of this, weak negative influence was visible in

summer (Fig. 8). Distinct negative influence on Cladocera (partly on Copepoda) was exerted by

nanoplanktonic Chrysophyceae and Euglenophyceae. Most clearly this impact was visible in winter,

and less in summer. A positive influence on Rotifera was exerted by the nanoplanktonic

Bacillariophyceae, but less by the microplanktonic Conjugatophyceae, Chrysophyceae and

Chlorophyceae. This influence was visible in all seasons, however, less frequently in summer, when

it was often negative (Fig. 8).

Discussion

One of the most important associations affecting phytoplankton abundance and biomass in lakes

is zooplankton (Kawecka and Eloranta, 1994). Negative relationship between these two groups is

expected, which is the result of predation by zooplankton on phytoplankton. A positive relationship

can indicate that phytoplankton growth can be stimulated by zooplankton. Such relationships were

obtained by simple and multiple regression analyses and partially by canonical correlation analyses

and redundancy analyses. Canonical correlation analysis suggests the 26% of variance of

phytoplankton biomass, divided into two size groups, can be explained by zooplankton variables

(grazing rate, rotifers and copepods biomass). Taking into account 14 phytoplankton groups it is

possible to explain 16.5% of this phytoplankton variance, i.e. similar to water temperature. Because

most phytoplankton and zooplankton variables are temperature dependent, a clearer result is

probably shown by RDA analysis, in which water temperature was used as a covariable. This

analysis confirms the positive influence of zooplankton variables on Cryptophyceae (mainly

grazing rate) and Conjugatophyceae (Copepoda biomass). The Cryptophyceae due to their flagella

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are possible to escape the grazing pressure of filtrators, while Conjugatophyceae are probably too

small to be good prey for predatory copepods. This positive influence of grazing rate on species

belonging to Cryptophyceae was proved by simple regression analysis and was probably connected

with nutrient release by zooplankton, which stimulate algal growth (Kawecka and Eloranta, 1994).

This influence was also proved by calculated results of nutrient excretion by zooplankton

(Kowalczewska-Madura et al., 2007), which together with internal loading from bottom sediments

explained 33% of variance of the phytoplankton variables.

RDA did not confirm the positive ralationship between zooplankton grazing and Cyanobacteria,

which was probably the effect of autocorrelation. The negative effect shown in summer (Fig. 7) is

probably connected with grazing of Cladocera on Cyanobacteria, due to lack of more suitable food.

This is consistent with the laboratory experiments of Dawidowicz et al. (Dawidowicz et al., 1988)

that large cladocerans may feed on colonial Cyanobacteria.

The negative influence of Rotifera on nanoplanktonic algae resulting from RDA is in agreement

with statement of Karabin (Karabin, 1985) and Telesh (Telesh, 1993), that these algae can be easily

digested by rotifers. Tadonleke et al. (Tadonleke et al., 2004) have noted such pressure of rotifers

on heterotrophic nanoflagellates, and Jürgens & Jeppesen (Jürgens and Jeppesen, 2000) also on

small ciliates and autotrophic picoplankton, which were not taken into account in the present study.

Trophic relationship may also explain the negative influence of Copepoda on microplanktonic

algae, especially Dinophyceae, using RDA analysis. Copepoda as reported by Sommer et al.

(Sommer et al., 2003) may suppress these algae.

The canonical correlation analyses suggest that phytoplankton, especially when divided into 14

groups, can explain as many as 67.7-88.3 % of the variance for the zooplankton variables (Table

III). This may be probably the effect of autocorrelation, because RDA did not confirm such

intensive influence. In both analyses, however, the positive effect of Cryptophyceae and

microplanktonic Cyanobacteria on Cladocera was demonstrated. It is difficult to explain these

relationships, because Cryptophyceae are not easy available for Cladocera. Cyanobacteria may

represent a food of good quality in some cold months, but during summer many species are

potentially toxic. This may explain why RDA displayed the weak negative effect of

microplanktonic Cyanobacteria on cladoceran biomass in summer. Such influence of Cyanobacteria

was earlier reported e.g. by Jungmann and Benndorf (Jungmann and Benndorf, 1994), Reinikainen

et al. (Reinikainen et al., 1995), Fradkin and Gilbert (Fradkin and Gilbert, 1996). The reason there

is a negative influence of nanoplanktonic Chrysophyceae and Euglenophyceae on Cladocera is not

evident since they are considered a good food source for crustaceans (Kawecka and Eloranta, 1994).

This is possibly the effect of autocorrelation with other, unknown variables. These ecological

variables may include top-down pressure of fish, interactions between zooplankton species,

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presence of macrophytes and various chemical compounds produced in the lake, or introduced into

these waters from the catchment area (Jürgens and Jeppesen, 2000; Jeppesen et al., 2002).

The domination of small species in the zooplankton community can be associated with fish

predation pressure and by the negative influence of cyanobacteria. Cyanobacteria clearly prevailed

in the phytoplankton during the summer of the first two years of this study. When their numbers

exceed a threshold value, they could exert a negative influence on the feeding, development, and

abundance of large cladocerans. Also, cyanobacterial filaments make their foraging difficult (they

block the closing of the carapace), so these algae can influence the decline of the cladoceran

community (Dawidowicz, 1990). Larger-sized cladocerans (mainly Daphnia spp.) were quite

abundant, but mainly in spring. According to Meijer (Meijer, 2000), in some conditions they can

contribute to the low level of phytoplankton biomass despite a high trophic state of the water.

However, if phosphorus concentration is high and biomanipulation mechanisms fail, a sudden water

bloom may appear (Gołdyn et al., 1997). In Swarzędzkie Lake, we observed a similar rapid decline

of cladoceran biomass, accompanied by accompanying rise in cyanobacteria abundance. In August

2001, cladoceran numbers decreased to 18 ind. L-1, while numbers of cyanobacteria and green algae

increased. This unfavourable influence of cyanobacterial blooms on cladoceran communities

(especially on Daphnia longispina) has been observed in a eutrophic lake in Portugal (Abrantes et

al., 2004). A cyanobacterial bloom in summer also inhibited zooplankton development in the

Siemianówka Reservoir (NE Poland) (Górniak and Grabowska, 1996).

In 2002, cladoceran abundance did not decline, resulting in the calculated grazing rate that

reached an unusually high value of 150.6 % d-1. Cyanobacterial abundance and biomass were then

lower than in preceding years, and probably because cladocerans controlled their numbers. This

control of filamentous cyanobacteria growth by abundant large-sized cladocerans was reported by

Gołdyn et al. (Gołdyn et al., 1997). This relationship is associated with the active breaking of single

cyanobacterial filaments by the zooplankton, which can then easily feed upon the cyanobacteria

(Gulati, 1990). However, it is likely that other variables (physico-chemical, hydrological or

biological) may have influenced both the concentrations of the cyanobacteria and cladocerans with

the grazing rate the response to a lack of cyanobacteria in the ecosystem.

The high grazing rates in the summer of 2002 also coincided with the greatest phytoplankton

biomass at that time. Cyanobacteria dominance was replaced by dinoflagellates, with Ceratium

hirundinella the dominant species. The large size of this species prevented its consumption by

filter-feeding zooplankton, so the calculated grazing rate is potential rather than real. This was

probably caused by incomplete filtration, and the high density of cladocerans, which negatively

affected the feeding rate (Helgen, 1987). Similar water blooms caused by large dinoflagellates

(including C. hirundinella) were observed by van Ginkel et al. (van Ginkel et al., 2001), Tomec et

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al. (Tomec et al., 2002), and Grigorszky et al. (Grigorszky et al., 2003). Ceratium hirundinella is

able to reach high numbers and biomass associated with its diel migrations in the vertical profile.

As reported by Frempong (Frempong, 1984), it can migrate for distances of up to 5 m per day.

Whittington et al. (Whittington et al., 2000) note that the velocity of migration of this species is

0.57-0.97 m h-1. This allows active photosynthesis in the surface layer of water at optimal light

intensity, followed by absorption of nutrients near the bottom during other periods. Ceratium

hirundinella also provides a suitable food source for advanced copepodite instars and adult

cyclopoid copepods (Santer, 1996; Sommer et al., 2003), in addition to predatory rotifers such as

Anapus species (Starmach et al., 1976). These predators, however, were not abundant in

Swarzędzkie Lake in the 2002 summer, so their control was considered ineffective. An additional

reason may be a predatory preference by the cyclopoid copepods for ciliates rather than algae in a

hypertrophic lake (Jürgens and Jeppesen, 2000).

The comparison of grazing rates calculated according to Knoechel and Holtby’s and Lampert’s

models showed that the former may over estimate the rates. For instance, the high value for May

2002 (150.6 % d-1) suggests phytoplankton net growth was fully controlled by zooplankton at that

time. However, this is not consistent with the relatively high abundance and biomass of

phytoplankton recorded then. The grazing rate calculated from Lampert’s model for that month

(87.56 % d-1) appears more realistic.

In conclusion, the distinct influence of zooplankton grazing and predation on phytoplankton

abundance and biomass was not apparent in this highly eutrophic lake, in comparison to results

obtained in enclosure experiments by other authors (Sommer et al., 2003; Stibor et al., 2004;

Sommer U., Sommer F., 2006). As we expected zooplankton suppress nanoplanktonic species, but

not from all taxonomic groups. RDA proved only weak influence on nanoplanktonic

Euglenophyceae and Chlorophyceae exerted by filtering crustaceans and on Cyanobacteria and

Chysophyceae by rotifers. Simple regression proved that only some sensitive species were

significantly suppressed by zooplankton. Due to RDA there was indicated an unexpected distinct

negative influence (suggested grazing) of filtrators exerted on microplanktonic Cyanobacteria

during summer. However, even in the period of intensive grazing, no ‘clear water phase’ was

observed in the lake but only a shift in dominating phytoplankton groups from cyanobacteria to

dinophytes. Positive influence of zooplankton grazing on Cryptophyceae (both micro- and

nanoplanktonic) detected by RDA was a confirmation of results of simple regression between

grazing rate and species belonging to Cryptophyceae. It indicated a stimulation of growth of species

resistant to selective grazing or predation by zooplankton.

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Acknowledgements:

The study was supported by the Polish Committee for Scientific Research (grant no. 3PO4FO1724).

Special thanks are extended to Dr. Marek Kasprowicz, for his help in RDA the analyses. We also

thank the anonymous reviewer for many comments that helped improve the original manuscript.

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Table legends:

Table I. Morphometric data of Swarzędzkie Lake (Kowlaczewska-Madura, 2005; Szyper et al.,

1994)

Table II. Results of canonical correlation analyses (statistically significant cases were only

presented) (No. of valid cases = 28)

Table III. The canonical factor loadings and weights of zooplankton variables (left set) and of

biomass of two phytoplankton size groups (right set) as a result of canonical correlation analysis

presented in Table II. Only statistically significant data of the 1st canonical root are given.

Table IV. Example of canonical factor loadings and weights of particular variables as a result of

canonical analysis of three zooplankton variables versus 14 phytoplankton groups, presented in

Table II. Only statistically significant data of the 1st canonical root are given.

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Figure legends:

Fig. 1. Abundance of phytoplankton groups in Swarzędzkie Lake in 2000−2002.

Fig. 2. Biomass calculated from biovolumes of phytoplankton groups in Swarzędzkie Lake in

2000−2002.

Fig. 3. Chlorophyll a (bars) and biomass of two phytoplankton size groups: nanoplankton (below

30 µm) and microplankton (over 30 µm) in the water layer just below the surface of Swarzędzkie

Lake in 2000−2002.

Fig. 4. Abundance (means for the vertical profile) of rotifers (a), cladocerans (b), and copepods (c)

in Swarzędzkie Lake in 2000−2002.

Fig. 5. Biomass of rotifers, cladocerans and copepods (means for the vertical profile) in

Swarzędzkie Lake in 2000−2002.

Fig. 6. Zooplankton community grazing rates calculated by Knoechel and Holtby's (K&H) model

(Knoechel and Holtby, 1986) and Lampert's (L) model (Lampert, 1988) and chlorophyll a

concentration in Swarzędzkie Lake during the study period (means for the vertical profile).

Fig. 7. Triplot diagram (including 14 phytoplankton groups, 3 zooplankton variables and 28

samples) for redundancy analysis (RDA) of Swarzędzkie Lake data. Temperature data were used as

a covariable. Acronyms: LAM – grazing rate data by the Lampert’s model, mic – microplanktonic,

nan – nanoplanktonic, CYA – Cyanobacteria, EUG – Euglenophyceae, CRY – Cryptophyceae, DIN

– Dinophyceae, CHR – Chrysophyce, BAC – Bacillariophyceae, CON – Conjugatophyceae, CHL –

Chlorophyceae, black squeres – summer samples (July-August), empty rectangles – autumn

samples (September-Oktober), empty diamond – winter samples (November-March), black stars –

spring samples (April-June)

Fig. 8. Triplot diagram for RDA including phytoplankton groups (explanatory variables),

zooplankton biomass (dependent variables) and samples. Only 8 groups of phytoplankton more

statistically significant were shown. Temperature data were used as a covariable. Acronyms: see

Fig. 7.

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Tables

Table I

Parameter Unit Value

Total area ha 93.70

Area of open water ha 79.40

Volume 106 m3 2.00

Mean depth m 2.60

Maximum depth m 7.20

Maximum length km 2.94

Maximum width km 0.56

Mean water residence time d 47.0

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Table II

Index of redundancy Left set Right set p

Left set Right set Can. R

K&H+Rot+Cop (m.v.)* nan + mic 0.22 18.79 26.08 0.60

K&H (s.v.) 14 phyt. groups 0.008 79.30 6.00 0.89

Lam (s.v.) 14 phyt. groups 0.006 80.22 5.87 0.89

K&H (m.v.) 14 phyt. groups 0.0002 88.34 7.08 0.94

Lam (m.v.) 14 phyt. groups 0.013 77.41 5.96 0.88

K&H+Rot (m.v.) 14 phyt. groups 0.003 75.90 13.10 0.94

K&H+Rot+Cop (m.v.)* 14 phyt. groups 0.002 67.75 16.50 0.96

Temp (s.v.) 14 phyt. groups 0.002 83.27 13.55 0.91

p – probability level, K&H – community grazing rates calculated by Knoechel and Holtby's model, Lam –

Lampert’s model, Rot – biomass of Rotifera, Cop – biomass of Copepoda without Calanoida, s.v. – value

from the sample taken just below the water surface, m.v. – mean value from vertical profile, Temp – water

temperature data, nan – nanoplanktonic biomass, mic – microplanktonic biomass, 14 phyt. groups – biomass

of 14 phytoplankton groups explained in Table IV, * – examples presented in details in Table III and IV.

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Table III

Variable Canonical factor

loadings Canonical weights

Left set K&H -0.851 -1.004

Rot -0.552 -0.047

Cop 0.214 0.556

Right set nan -0.174 -0.019

mic -0.999 -0.997

Acronyms – see Table II.

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Table IV

Variable

Canonical factor

loadings Canonical weights

Left set K&H -0.920 -1.200

Rot -0.413 0.263

Cop 0.013 0.349

Right set Cyanobacteria-nan -0.351 -0.343

Euglenophyceae-nan 0.488 0.563

Cryptophyceae-nan -0.342 -0.213

Chrysophyceae-nan 0.407 0.363

Bacillariophyceae-nan -0.075 -0.189

Chlorophyceae-nan -0.207 0.127

Cyanobacteria-mic -0.182 -0.375

Euglenophyceae-mic 0.110 0.076

Cryptophyceae-mic -0.510 -0.441

Dinophyceae-mic -0.098 -0.200

Chrysophyceae-mic 0.011 -0.214

Bacillariophyceae-mic -0.011 -0.232

Chlorophyceae-mic 0.039 0.214

Conjugatophyceae-mic -0.237 -0.279

Acronyms – see Table II.

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Figures

0

10000

20000

30000

40000

50000

60000

J J A S O N D J F M A M J J A S O N D J F M A M J J A S

spec

imen

s m

L-1

othersChlorophyceaeBacillariophyceaeDinophyceaeCryptophyceaeCyanobacteria

61,300

2000 2001 2002

Figure 1.

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0

10

20

30

40

50

60

70


mg

WW

L-1

Cyanobacteria CryptophyceaeDinophyceae BacillariophyceaeChlorophyceae others

99.5

2000 2001 2002

Figure 2.

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01020304050607080


mgW

W L

-1

0

20

40

60

80

100

120

⎯g C

hla

l -1

MicroplanktonNanoplanktonChlorophyll a

2000

99.5

2001 2002Figure 3.

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0100020003000400050006000700080009000

10000


ind.

L-1

othersPompholyx sulcata Synchaeta pectinata + S. oblongaKeratella cochlearis cochlearisKeratella cochlearis tecta Keratella quadrata

18000

2000 2001 2002

A

050

100150200250300350400450500


ind.

L-1

othersDaphnia cucullata Bosmina longirostrisBosmina coregoniChydorus sphaericus

721

2000 2001 2002

B

0100200300400500600700800900

1000


ind.

L-1

othersNaupliiCopepoditesMesocyclops leuckarti

2000 2001 2002

C

Figure 4

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0100200300400500600700800900


μgD

W L

-1

CopepodaCladoceraRotifera

2000 2001 2002

Figure 5.

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020406080

100120140160

J J A S O N D J F M A M J J A S O N D J F M A M J J A S0102030405060708090K&H model

L modelchlorophyll-a

Gra

zing

rate

(% d

-1)

μg C

hla

L-1

2000 2001 2002

Figure 6.

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Figure 7.

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Figure 8.

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