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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
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
mg
WW
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Cyanobacteria CryptophyceaeDinophyceae BacillariophyceaeChlorophyceae others
99.5
2000 2001 2002
Figure 2.
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01020304050607080
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
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
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
ind.
L-1
othersPompholyx sulcata Synchaeta pectinata + S. oblongaKeratella cochlearis cochlearisKeratella cochlearis tecta Keratella quadrata
18000
2000 2001 2002
A
050
100150200250300350400450500
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
ind.
L-1
othersDaphnia cucullata Bosmina longirostrisBosmina coregoniChydorus sphaericus
721
2000 2001 2002
B
0100200300400500600700800900
1000
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
ind.
L-1
othersNaupliiCopepoditesMesocyclops leuckarti
2000 2001 2002
C
Figure 4
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0100200300400500600700800900
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
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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