Hydrobiologia

, Volume 715, Issue 1, pp 37–50 | Cite as

Seasonal dynamics of sestonic protease inhibition: impact on Daphnia populations

  • Christian J. Kuster
  • Anke Schwarzenberger
  • Eric von Elert
CLADOCERA

Abstract

Daphnia populations often show rapid microevolutionary adaptation to environmental changes. Here, we investigated the possibility that microevolution of Daphnia populations could be driven by natural sestonic Protease Inhibition (PI). We hypothesized that PI changes seasonally, which might lead to concomitant changes in tolerance to PI in a co-occurring Daphnia magna population. In order to test this, seston from a eutrophic pond was sampled regularly over two successive years. Extracts of these freeze-dried samples were used to determine their Inhibitory Potential (IP) on D. magna gut proteases. In the summer seston the IP against chymotrypsins exceeded that of spring seston 200-fold. In order to test for possible impacts on the co-existing D. magna population, we isolated clones before (spring) and after (fall) the peak of the IP. Microsatellite analyses revealed that the two subpopulations were genetically distinct. Individual exposure of three clones from each population to varying concentrations of a cyanobacterium that contains chymotrypsin inhibitors revealed a decrease in population and somatic growth rate for each clone, but no seasonal effects on Daphnia’s tolerance. In order to include maternal effects, we conducted a multi-clonal competition experiment on various cyanobacterial concentrations. However, no evidence for seasonally increased tolerance of D. magna to dietary protease inhibitors could be found.

Keywords

Daphnia Cyanobacteria Chymotrypsin Trypsin Inhibition Microevolution 

Introduction

Due to anthropogenic phosphorus loading, many lakes have undergone a period of eutrophication in the last decades (Correll, 1998; Daniel et al., 1998). The phytoplankton of eutrophic lakes is usually dominated by bloom-forming species of cyanobacteria of the genera Microcystis, Planktothrix, Limnothrix, Anabaena, or Oscillatoria (Schreurs, 1992; Dokulil & Teubner, 2000). A considerable accumulation of cyanobacterial biomass can be observed in late summer and early fall (Sommer et al., 1986), when the temperature of the epilimnion reaches its maximum (Johnk et al., 2008). Abundances of zooplankton, especially of non-selective filter feeders such as Daphnia (one of the main grazers of cyanobacteria), are highly influenced by the presence of cyanobacteria. The increasing dominance of cyanobacteria has been claimed to be a major factor leading to the decline in Daphnia abundances within and between lakes (Threlkeld, 1979; Hansson et al., 2007). Although Daphnia populations and individuals can adapt to the presence of cyanobacteria by developing a higher tolerance to the occurrence of cyanobacteria (Gustafsson & Hansson, 2004; Hairston et al., 1999; Hairston et al., 2001; Sarnelle & Wilson, 2005), the traits underlying this adaptation have not yet been fully elucidated.

The causes for the poor assimilation of cyanobacterial carbon by Daphnia have been studied extensively in the past decades. Three major food quality constraints have been revealed: (1) Cyanobacteria can be filamentous or colonial; both forms lead to mechanical interferences with the filtering process of daphnids (Porter & Mcdonough, 1984). (2) Cyanobacteria lack sterols and have a low polyunsaturated fatty acid content; both are essential for Daphnia nutrition (Von Elert, 2002; Von Elert et al., 2003). In Daphnia, this deficiency in lipids leads to reduced somatic growth and reproduction of daphnids due to constrained carbon assimilation (Von Elert & Wolffrom, 2001; Martin-Creuzburg & Von Elert, 2004; Martin-Creuzburg et al., 2008). (3) Many cyanobacterial strains also contain toxins such as microcystins, which have a negative effect on the fitness of Daphnia (Sivonen & Jones, 1999; Rohrlack et al., 2001). However, a microcystin-free cyanobacterial strain of the genus Microcystis resulted in reduced growth of Daphnia, even though mechanical interferences and lipid deficiency could be excluded as causal factors (Lürling, 2003). The reduced growth rates might be explained by the presence of other secondary metabolites in cyanobacteria that inhibit survival, growth, reproduction, and/or feeding of Daphnia (Lürling & van der Grinten, 2003; Rohrlack et al., 2003). Among such secondary metabolites, protease inhibitors are reported from a wide range of genera of marine and freshwater cyanobacteria. More than twenty depsipeptides which act as protease inhibitors have been described from different cyanobacteria genera (Gademann & Portmann, 2008). Most of these depsipeptides specifically inhibit the serine proteases chymotrypsin or trypsin: These two digestive enzymes are responsible for 80% of the proteolytic activity in the gut of Daphnia magna (Von Elert et al., 2004), and several studies indicate that cyanobacterial protease inhibitors directly inhibit digestive proteases of Daphnia (Agrawal et al., 2005; Schwarzenberger et al., 2010). The study by Czarnecki et al. (2006) has shown that the potential of natural seston to inhibit Daphnia trypsins is comparable to the inhibitory potential of various Microcystis strains. This makes it reasonable to assume that the interference of cyanobacterial protease inhibitors with digestive proteases of Daphnia, which had thus far only been demonstrated for cyanobacterial strains, occurs in nature as well. Blom et al. (2006) recently showed that Daphnia sp. coexisting with Planktothrix rubescens, a cyanobacterium that contains the trypsin inhibitor oscillapeptin J, are significantly less sensitive to oscillapeptin J than Daphnia sp. from lakes free of this cyanobacterium. Considering the finding that almost 60% of 17 water blooms of cyanobacteria contained protease inhibitors (Agrawal et al., 2001), it is thus reasonable to assume that increased tolerance to cyanobacteria in Daphnia populations may be caused by enhanced tolerance to the cyanobacterial protease inhibitors. As a consequence, the question arises as to whether this increased tolerance at the population level is caused by a constant selection pressure due to cyanobacterial protease inhibitors or by a short-term bottleneck effect due to the seasonal occurrence of cyanobacteria.

We hypothesized that, in accordance with the seasonal succession of phytoplankton, the potential of the edible fraction of a natural seston to inhibit digestive trypsins and chymotrypsins in Daphnia would vary with season. This seasonality would result in increased frequencies of Daphnia genotypes with enhanced tolerance to cyanobacterial protease inhibitors.

In the present study, we investigated whether the potential of the natural seston to inhibit the proteases trypsin and chymotrypsin in D. magna changed seasonally. Furthermore, we determined the clonal diversity of two D. magna samples, isolated before and after the maximum of inhibitory potential in the seston, using microsatellites to detect concurrent changes in the corresponding D. magna population. In addition, we conducted growth experiments with single D. magna clones from each of the two samples on treatments differing in the content of cyanobacteria with protease inhibitors. In order to investigate competitive interactions and maternal effects at different cyanobacterial protease inhibitor concentrations, we also performed a multi-clone experiment with the same clones over many generations.

Specifically, our predictions were that: (1) The potential of natural seston to inhibit Daphnia chymotrypsins and trypsins changes seasonally. In accordance with the seasonal occurrence of cyanobacteria, we expect a higher potential of seston to inhibit proteases in fall than in spring. (2) D. magna clones from the fall are more tolerant to cyanobacterial protease inhibitors, which results in higher growth rates on cyanobacterial diets in single-clone experiments. Potential costs of this adaption might result in lower growth rates and fitness on non-cyanobacterial diets. (3) D. magna clones from the fall show higher clonal frequency in multi-clonal experiments on a cyanobacterial diet containing protease inhibitors than clones from the spring.

Materials and methods

Origin and cultivation of organisms

Two species of phytoplankton were used in the somatic and population growth experiments as well as in the competition experiment: The cyanobacterium Microcystis aeruginosa NIVA Cya 43 (Culture Collection of Algae, Norwegian Institute for Water Research) was grown in 2 l chemostates in sterile Cyano medium (Von Elert & Jüttner, 1997) at a dilution rate of 0.1 day−1 (20°C; illumination: 40 μmol m−2 s−1). The green algae Chlamydomonas sp. (Strain 56, culture collection of the Limnological Institute at the University of Konstanz) was grown in 5 l semi-continuous batch cultures (20°C; illumination: 120 μmol m−2 s−1) by replacing 20% of the culture with fresh sterile Cyano medium every Monday, Wednesday, and Friday in the late exponential phase of the culture.

The D. magna clones were isolated from the Aachener Weiher (AaW; N50°56′2.40″, E6°55′40.81″). The AaW is an urban pond of 0.04 km2 and a maximum depth of 1.6 m. The AaW has a Secchi depth of less than one meter, and is, therefore, classified as a hypertrophic lake (Carlson, 1977). Forty-seven D. magna clones were isolated on June 4, 2008 (spring samples) and 43 clones on September 3, 2008 (fall samples). D. magna clone B used in the enzyme assay originated from the Großer Binnensee, a lake in Germany (Lampert & Rothhaupt, 1991). All clones of D. magna were cultured separately in aged, membrane-filtered tap water and fed with saturating concentrations of Chlamydomonas sp. for at least three generations.

Preparation of the seston samples

In order to test the potential of the edible fraction of seston (<55 μm) in the AaW to inhibit proteases, samples were taken at constant intervals from spring to fall in 2007 and 2008. Fifty to 80 l of surface water was screened with a 55 μm mesh. In the discharge, the particulate fraction was then gently concentrated using a hollow-fiber filtration technique (A/G Technology Corp., Needham, CFP-1-D-6a, 0.1 μm mesh size). The final volume of approximately 0.8 l was frozen at −20°C and freeze-dried (Christ LOC-1 m, LPHA 1-4). Lyophilized seston was subsequently homogenized using a mortar and pestle. The resulting powder was thoroughly mixed. 50 mg and 100 mg of freeze-dried natural seston were suspended in 500 μl of 60% methanol and sonicated for 15 min followed by centrifugation (3 min at 10,000×g). The extract was separated by centrifugation from the residue, and the supernatant was used in the protease assay.

Protease assays

The activities of trypsins and chymotrypsins of D. magna clone B were measured according to Von Elert et al. (2004). Six-day-old individuals were each transferred to 5 μl (per animal) of cold (0°C) 0.1 M phosphate buffer (pH 7.5) containing 2 mM dithiothreitol (DTT) on ice. 2 mM DTT in the incubation buffer was used according to Johnston et al. (1995). Individuals were homogenized with a Teflon pestle, the homogenate was centrifuged (3 min at 14,000×g), and the supernatant was used immediately in the enzyme assay. SuccpNA (N-succinyl-L-alanyl-L-alanyl-L-prolyl-l-phenylalanine 4-nitroanilide, Sigma, 125 μM) was used as a substrate for chymotrypsins, while BapNA (N-R-benzoyl-DL-arginine 4-nitroanilide hydrochloride, Sigma, 1.8 mM) served as a substrate for trypsins. Trypsin and chymotrypsin assays were performed in a potassium phosphate buffer (0.1 M, pH 7.5). The absorption was measured continuously for 10 min at time intervals of 30 s at 30°C at 390 nm with a Cary 50 photometer (Varian, Palo Alto, USA). Absorption increased linearly with time in all assays. The relevant parameter for the enzyme assays was the inhibition potential of the seston samples. In order to ensure comparable conditions for all protease assays, we normalized the protease activities to the same conversion rate of respective substrates prior to the addition of aliquots of the seston extracts. Five to 20 μl of different concentrations of each extracted seston biomass were tested for inhibition, whereas controls with 20 μl of 60% methanol had no effects on protease activities. The resulting protease activities were plotted as a function of extracted seston biomass. By fitting a sigmoidal dose response curve, the extracted biomass that resulted in a 50% inhibition of Daphnia protease activity (IC50) was calculated. Low IC50 values of the analyzed samples indicated a high inhibitory potential.

Microsatellite analyses

The population structure of the spring and the fall D. magna populations were analyzed using Polymerase Chain Reactions (PCR) of six polymorphic microsatellite loci with subsequent DNA fragment length analyses. The PCR assay for each D. magna clone was performed in a final volume of 50 μl containing 50 ng of genomic DNA, 0.2 μM of respective forward and reverse primer (Table 1), 0.2 mM of dNTPs, 2.5 units of Taq-DNA polymerase (5 Prime, Gaithersburg, USA), and 5 μl of corresponding 10× PCR buffer (5 Prime, Gaithersburg, USA). Cycling parameters were 95°C for 5 min to activate the DNA polymerase followed by 35 cycles of denaturation for 30 s at 95°C, annealing for 30 s at respective primer Ta (Table 1), and 30 s elongation at 72°C. Final elongation was performed afterward for 10 min at 72°C. After checking the success of the amplification via agarose gel electrophoresis, DNA fragment length analyses were performed with a 3730 DNA Analyzer (Applied Biosystems, Foster City, USA).
Table 1

Microsatellite loci used to genotype the D. magna clones, the respective forward and reverse sequences, GenBank accession number, and the corresponding annealing temperatures (Ta)

Locus

Primer sequences [5′–3′]

GenBank accession no.

Ta (°C)

DMA 12

F: AGCCAATCATCAATTCCCTC

AF291912a

58

R: AAGGTCCGAATTGGATTACG

  

DMA 3

F: AAAGGAAAGCAACCGCTGC

AF291910a

58

R: AAAAGGAAGGGGAATTACCC

  

DMA 14

F: GGGCAAGACACAGGTGC

AF291913a

58

R: TGGCGGCATGCTGTCTAC

  

DP 162

F: CGAATCCGTTCGTCAAAAGC

wfms0000166b

58

R: TGGCGGCATGCTGTCTAC

  

S6-38

F: GATGTCTTGCATCAACAGTG

S6-38c

49

R: AGTCAAAGGTATGACTCACC

  

DMA 15

F: GTGTATTCTAAAATCGAATTTCG

EU131363a

50

R: ACGTCAATGATCTTATTATACC

  

aDeveloped by John Colbourne, unpublished

bColbourne et al. (2004)

cOrtells et al. (2012)

Somatic and population growth assays

Three D. magna clones of each sample (spring: clones S2, S7, S15; fall: clones F3, F11, F24) were used in the growth experiments. These clones were chosen because they could be distinguished based on three microsatellite loci (S6-38, DP-162, and DMA-12, see Table 1). The individuals originated from mothers which had been raised under controlled conditions (saturating concentrations of Chlamydomonas sp.) for at least three generations.

Each of the six clones was assayed in single-clone experiments for somatic and population growth. Juveniles from the same cohort of the second to the fourth clutch were collected within 24 h of birth. For the growth experiments, six juveniles were kept in 0.25 l of aged and filtered tap water (membrane filter of 0.45 μm pore size) under dim light at 20°C. In order to rule out quantitative food limitation, the animals in all treatments were fed daily with 2 mg C/l. In one treatment, the animals were fed with 100% Chlamydomonas sp.; in two further treatments, the animals were fed with 2 mg C/l of either a mixture of 90% Chlamydomonas sp. and 10% M. aeruginosa or a mixture of 80% Chlamydomonas sp. and 20% M. aeruginosa. Each treatment was replicated three times. Animals were transferred daily into fresh water and given fresh food to exclude the possibility of quantitative food limitation. Somatic growth rates were calculated as according to Wacker & Von Elert (2001) as
$$ g = [\ln (G_{t} ) - \ln (G_{0} )]/d, $$
in which G is the body weight of a subsample of the animals at the beginning (G0) and end (Gt) of the experiment. Mean individual dry weights were mean values of two individuals. Population growth rates were calculated from daily survival and fecundity of the first clutch using Euler’s equation,
$$ 1 = \sum l_{x} m_{x} e^{ - lx} $$
in which lx is the survival rate and mx the size of the first clutch at day x. Population and somatic growth rates were calculated for each replicate and subsequently averaged to give the mean of the treatment.

Competition experiment

Twelve neonates of each of the six D. magna clones (S2, S7, S15, F3, F11, F24) were cultured together in containers filled with 10 l of aged and constantly aerated tap water under dim light at 20°C. The same cohort of newborns that was used for the single-clone experiment was used to inoculate the competition experiment. Animals were fed either with 100% Chlamydomonas sp. or with a mixture of 90% Chlamydomonas sp. and 10% M. aeruginosa. Each treatment was replicated five times. In order to eliminate possible negative effects due to food quantity during the course of the experiment, the food concentration in all replicates was measured daily with a particle counter (CASY, Innovatis AG, Reutlingen) and a corresponding calibration curve. When the food concentration in the treatments dropped to 0.5 mg C/l, supplementary food was added to keep the food concentration constantly above 0.2 mg C/l, the incipient limiting food level for D. magna (Porter et al., 1982). When daily carbon consumption in the experiment containers reached 3 mg C/l day−1, the animal density was reduced through dilution. A dilution was performed to avoid size-selective effects: First, the water was thoroughly mixed; 30–50% of the volume, including animals, was subsequently removed, transferred to a new container, and then topped off with fresh water and food. Thus, animal densities never exceeded 60 individuals/l. In addition, the whole water body was changed twice a week by screening the animals through a 200 μm mesh and directly transferring them into a container with fresh water and food. The screening process was performed as gently and quickly as possible to minimize mortality and selection effects due to this procedure.

The experiment was terminated after 90 days. Following Weider et al. (2005), ~50 individuals were sampled randomly on days 30 and 60 as well as at the end of the experiment, and subsequently genotyped using three microsatellite loci (S6-38, DP-162, and DMA-12; see Table 1). The mean frequency of each clone was calculated in both treatments for each of the three sampling times.

Data analysis

For the analysis of protease inhibition potential of the natural seston, the protease activities were plotted as a function of extracted seston biomass. Resulting IC50 values with corresponding 95% confidence intervals (CI) were calculated by fitting a sigmoid dose–response curve using the software GraphPad Prism (GraphPad Software, Inc.). IC50 values with non-overlapping 95% CI were regarded as different.

The expected heterozygosity (He) and the observed heterozygosity (Ho) of the spring and the fall samples were calculated using ArlequinV. 3.11 (Excoffier et al., 2005). An unbiased estimate of the exact P-value was computed using the Markov-chain method of Guo & Thompson (1992), with the chain set of 1,000 dememorization steps and 100,000 iterations. Microsatellite data were further used for calculation (Arlequin) of pairwise FST (Reynolds et al., 1983).

Analysis of variance (ANOVA) was used to analyze the effect of the diet on somatic and population growth. A single ANOVA was carried out for each clone for the somatic and population growth experiments; the growth rate was the dependent variable and diet was the independent variable. The dependent variable was checked for homogeneity of variances (Levene test) and, when necessary, log10 transformed prior to ANOVA. A significance level of P = 0.05 was applied to all statistical analyses. The effect of single treatments was tested by post hoc tests (Tukey’s HSD multiple-comparison test) on the same probability level as the respective analysis of variance.

In order to analyze the effect of the factors “diet” and “time” on the clonal frequency in the competition experiment, a repeated-measures analysis of variances (rm-ANOVA) was conducted using “frequency” as a dependent and “time,” “diet,” and “time x diet” as independent factors. The dependent variable was checked for homogeneity of variances (Levene test) and, when necessary, log10 transformed prior to ANOVA. We chose not to use sequential Bonferroni correction in multiple-comparison tests. The conservative nature of this adjustment often results in misclassification of ecologically relevant results (Moran, 2003). All statistical tests concerning growth and competition experiments were performed with STATISTICA v. 6.0.

Results

We found a seasonal variation of chymotrypsin inhibitory potential of the edible fraction of the AaW seston for 2007 and 2008 (Fig. 1a). In both years IC50 values decreased considerably in the summer, indicating a high inhibitory potential of the seston. From June to July 2007 a 200-fold decrease of chymotrypsin IC50 values was observed. In 2008, a significant decrease of IC50 was first measured for seston samples taken in August. The IC50 values remained on a comparatively low level until the end of September. Less seasonal variation was observed with regard to the inhibition of D. magna trypsins. A considerable decrease of trypsin IC50 values was only observed in 2007 (Fig. 1b). Contemporaneous with the chymotrypsins, the IC50 values for trypsins decreased significantly in July 2007. The IC50 values regarding the inhibition of trypsins remained at a low level for samples taken in 2008. However, seston samples taken in 2007 showed ten-fold lower IC50 values for the inhibition of D. magna chymotrypsins than that of trypsins. The seston of 2007 showed higher overall effects on both chymotrypsins and trypsins than the seston of 2008 did. When Chlamydomonas sp. was assayed for comparison, inhibitory effects were found neither for chymotrypsins nor for trypsins (IC50 > 104 μg/ml, data not shown). Additional information on all concentrations of extracted seston from 2007 and 2008 that were used for the determination of the IC50 values can be found in the supporting online material.
Fig. 1

Inhibition of digestive proteases from homogenates of D. magna clone B by dry weight extracts of the edible fraction (<55 μm) of seston of the AaW from different sampling dates. Seston samples from 2007 (black circles) and 2008 (white squares) were assessed for their effect on digestive chymotrypsins (a) and trypsins (b) of D. magna. IC50 concentrations of extracted freeze-dried seston (±95% CI) are depicted. IC50 values indicate the inhibitory potential of the seston sample. Low IC50 values indicate a high inhibitory potential of the seston, which means that little freeze-dried seston was needed to inhibit 50% of D. magna chymotrypsins. Non-overlapping 95% confidences intervals (CI) among two samples were assumed as significantly different. 95% CI may appear asymmetric due to log-scale of IC50 values. Where error bars are not visible, the 95% CI are smaller than symbols

In order to test for potential changes in clonal diversity of both D. magna samples, we analyzed 47 spring and 43 fall clones using six polymorphic microsatellite loci. We found 21 genetically distinct genotypes in the spring samples and 13 in the fall samples. Using exact P values, allele frequencies differed significantly (P < 0.05) from Hardy–Weinberg equilibrium in three loci of both populations (DMA 12, DP 162, and S6-38) due to increased heterozygosity (Table 2). On the contrary, the fall population indicates a deficit of heterozygous at loci DMA 3, DMA 14, and DMA 15 (P < 0.05), while the spring population only deviated significantly from Hardy–Weinberg equilibrium at locus DMA 3 (Table 2). The genetic distance between D. magna spring and fall samples was quite low (FST = 0.005); however, both samples were significantly distinct (P < 0.05).
Table 2

Observed (Ho) and expected (He) heterozygosity of the D. magna spring and fall samples in six polymorphic microsatellite loci, according to Hardy–Weinberg equilibrium

Locus

Population

N

Na

He

Ho

P

DMA 12

Spring

47

3

0.595

0.766

0.00019**

Fall

43

3

0.495

0.721

0.00011**

DMA 3

Spring

47

2

0.595

0.085

0.00000**

Fall

43

2

0.477

0.000

0.00000**

DMA 14

Spring

47

2

0.485

0.426

0.69900

Fall

43

2

0.385

0.512

0.04160*

DP 162

Spring

47

3

0.573

0.830

0.00033**

Fall

43

3

0.595

0.814

0.00013**

S6-38

Spring

47

2

0.374

0.489

0.04400*

Fall

43

2

0.484

0.791

0.00000**

DMA 15

Spring

47

2

0.446

0.425

0.0971

Fall

43

2

0.385

0.512

0.0415*

N number of analyzed clones, Na number of alleles. Significant differences at P < 0.05 (*) and P < 0.01 (**)

We performed growth experiments using three different diets to check for possible seasonal adaptation to chymotrypsin inhibition by the seston. Three genotypes of each D. magna sample were kept on three different diets: Animals were fed either with pure Chlamydomonas sp. or with one of two various mixtures of Chlamydomonas sp. and M. aeruginosa. We found an overall effect of diet on somatic growth rates of each of the D. magna clones (Fig. 2a; ANOVA, S2: F2,6 = 678.4; P < 0.001; S7: F2,6 = 66.83; P < 0.001; S15: F2,6 = 251.8; P < 0.001, F3: F2,6 = 219,7; P < 0.001; F11: F2,6 = 137.7; P < 0.001; F24: F2,6 = 1052.6; P < 0.001).

Somatic growth rates in M. aeruginosa treatments were significantly lower than in the control with pure Chlamydomonas sp. With increasing M. aeruginosa concentration, the growth rates of all tested clones decreased significantly (Tukey’s HSD, P < 0.05). Population growth (Fig. 2b) was also affected by diet (ANOVA, S2: F2,6 = 144.5; P < 0.001; S7: F2,6 = 57.0; P < 0.001; S15: F2,6 = 7.33; P < 0.001; F3: F2,6 = 28.7; P < 0.001; F11: F2,6 = 296.3; P < 0.001; F24: F2,6 = 227.6; P < 0.001). Similar to the somatic growth rates, the population growth rates of clones F11, F24, S2, and S7 decreased significantly with increasing M. aeruginosa concentration (Tukey’s HSD, P < 0.05). With regard to clones F3 and S15, only treatments with 20%M. aeruginosa resulted in lower population growth rates. The factor “season” affected neither the somatic nor the population growth rates.
Fig. 2

Somatic (a) and population (b) growth rates (mean ± SD, n = 3) of D. magna clones from the AaW on pure Chlamydomonas sp. and on two mixtures of Chlamydomonas sp. and M. aeruginosa. Clones labeled with “S” and “F” were isolated in spring and fall, respectively. Different significance levels (one Way ANOVA, Tukey’s HSD test, P < 0.05) within clones are indicated by different letters

In addition to the single growth experiments, a long-term experiment was performed to consider further effects due to maternal mechanisms or clonal interactions. D. magna clones were cultured together in two treatments differing in cyanobacteria content (Fig. 3). This competition experiment showed significant effects for factors “time” or “diet” on some of the six D. magna clones (Table 3). Clone S2 and F11 showed significantly lower relative abundances on day 90 than on day 30 on both pure Chlamydomonas sp. and on the mixture with 90% Chlamydomonas sp. and 10% M. aeruginosa (Fig. 3a, e). D. magna clone S7 was more abundant on the M. aeruginosa mixture than on pure Chlamydomonas sp. (Fig. 3b). None of the factors “time,” “diet,” and “time x diet” had significant effects on clones S15, F3, and F24 (Fig. 3 c, d, f). In order to exclude negative effects due to food quantity, the food concentration was always kept above 0.2 mg C/l, the incipient limiting food level of D. magna (Porter et al., 1982).
Fig. 3

Mean frequency (±SD, n = 5) of D. magna clones in the competition experiment. Clones were isolated from the AaW either in spring (ac) or in fall (df) 2008. Black squares and white circles represent frequency of clones grown on pure Chlamydomonas sp. or on a mixture of 90% Chlamydomonas sp. and 10% M. aeruginosa after 30, 60, and 90 days

Table 3

Results of repeated-measurement analyses of variances of D. magna clone frequency in control and Microcystis treatment

 

SS

df

F

P

Clone S2

 Treatment

0.002178

1

0.8252

0.393861

 Error

0.018475

7

  

 Time

0.064362

2

11.2087

0.001241**

 Time × treatment

0.000936

2

0.1631

0.851125

 Error

0.040195

14

  

Clone S7

 Treatment

0.143614

1

5.67122

0.048779*

 Error

0.177263

7

  

 Time

0.019229

2

0.73833

0.495626

 Time × treatment

0.004162

2

0.15980

0.853845

 Error

0.182306

14

  

Clone S15

 Treatment

0.085287

1

3.67251

0.096844

 Error

0.162562

7

  

 Time

0.016339

2

0.63204

0.546011

 Time × treatment

0.004847

2

0.18750

0.831080

 Error

0.180957

14

  

Clone F3

 Treatment

0.004433

1

0.63738

0.450891

 Error

0.048690

7

  

 Time

0.014072

2

2.22842

0.144476

 Time × treatment

0.000985

2

0.15592

0.857095

 Error

0.044204

14

  

Clone F11

 Treatment

0.005287

1

1.93804

0.206508

 Error

0.019096

7

  

 Time

0.008627

2

4.47832

0.031372*

 Time × treatment

0.001802

2

0.93546

0.415606

 Error

0.013484

14

  

Clone F24

 Treatment

0.016051

1

0.6264

0.454665

 Error

0.179381

7

  

 Time

0.031368

2

0.5769

0.574439

 Time × treatment

0.001483

2

0.0273

0.973145

 Error

0.380612

14

  

Significant differences at P < 0.05 (*) and P < 0.01 (**)

Discussion

Czarnecki et al. (2006) have recently shown that the potential for natural lake seston to inhibit trypsins of Daphnia was comparable to the potential of cultures of Microcystis sp. to inhibit trypsins. The present study showed for the first time that natural lake seston can also inhibit Daphnia chymotrypsins, which, in addition to trypsins, account for the largest proportion of proteolytic activity in the gut of D. magna (Von Elert et al., 2004). With regard to the well-known seasonal succession in the phytoplankton community (Sommer et al., 1986), our study is the first to investigate seasonal changes of the potentials of a natural lake seston to inhibit trypsins and chymotrypsins of D. magna. We focussed on the seston fraction smaller than 55 μm, which constitutes the edible size fraction for daphnids (Gophen & Geller, 1984; Hessen, 1985), and found seasonal changes in the potential of the AaW seston to inhibit D. magna chymotrypsins in each of the two successive years. From the end of June to mid-July 2007, this inhibitory potential increased more than 200-fold within merely 3 weeks. We found a similar pattern of chymotrypsin inhibition in 2008. Due to the lack of data about the phytoplankton composition in the AaW, it remains unclear as to whether or not the increase of the inhibitory potential was a consequence of a higher relative abundance of cyanobacteria containing protease inhibitors. However, we cannot rule out the possibility that this increase resulted from a relatively high cellular content of protease inhibitors within an otherwise unaltered phytoplankton community. Such increases of the content of particular secondary metabolites in response to growth conditions have already been demonstrated for microcystins in various Microcystis strains (Long et al., 2001; Wiedner et al., 2003).

In parallel with seasonal changes in the potential of the phytoplankton to inhibit proteases, the genetic structure of the D. magna population of the AaW changed in 2008. Microsatellite analyses of D. magna clones established in the laboratory showed that the spring and fall samples were genetically distinct. We found a decline in the number of D. magna genotypes from spring to fall. Due to selective differences among Daphnia clones, an erosion of clonal diversity during the parthenogenetic phase is a common phenomenon in Daphnia populations (Lynch, 1987; Vanoverbeke & De Meester, 2010). The loss of genetic variability in the AaW coincided with an increase of the inhibitory potential on chymotrypsins in the seston and could result from natural selection due to increasing protease inhibition on D. magna genotypes.

Three spring clones and three fall clones of D. magna were assayed to test whether seasonal changes in genotypes might be caused by the inhibitory potential of the seston on chymotrypsins. All six D. magna clones showed a significant reduction of their somatic growth rate with increasing cyanobacterial abundance in the food. However, only four of the six clones exhibited a reduction in population growth when feeding on 10% M. aeruginosa. Two clones showed no negative effects in population growth, even at a relative food abundance of 20% M. aeruginosa. This supports the data of Lürling (2003), who used the same strain of M. aeruginosa and reported a reduction in population growth of D. magna clones at a concentration of ≥25% M. aeruginosa. The M. aeruginosa strain used here produces the chemically known chymotrypsin inhibitors Cyanopeptolin 954 and Nostopeptin BN920 (Von Elert et al., 2005) and does not contain microcystins (Lürling, 2003). The observed negative effect of 20% of this cyanobacterium on somatic and population growth of the six D. magna clones suggests that this reduction was caused by an interference of the cyanobacterial inhibitors with the chymotrypsins in the gut of D. magna. Evidence that this strain of M. aeruginosa interferes with D. magna chymotrypsins is based on several studies (Von Elert et al., 2005; Schwarzenberger et al., 2010). Schwarzenberger et al. (2010) reported that the activity of gut chymotrypsins of D. magna decreased clearly when the animals were fed with M. aeruginosa. In addition, D. magna can respond with physiological plasticity to dietary cyanobacterial protease inhibitors by increasing the expression of the targets of these inhibitors, i.e., chymotrypsins, and by the expression of less-sensitive isoforms (Schwarzenberger et al., 2010, von Elert et al., 2012). Clearly these regulatory responses are adaptive for D. magna, as they increase the capacity for protein digestion in the presence of dietary protease inhibitors. These specific responses in chymotrypsin expression strongly point at an interference of the two known chymotrypsin inhibitors with D. magna chymotrypsins when this strain of M. aeruginosa is fed.

Variances in the mean tolerance to cyanobacteria of Daphnia populations over time have already been demonstrated. For example, Weider et al. (1997) noted significant genotypic shifts in the D. galeata population of Lake Constance, Germany, by collecting resting eggs from lake sediments dating from the mid-1960s to mid-1990s and analyzing the genotypic structure of the hatchlings from these resting eggs. The observed shifts in genotype composition were strongly correlated with changes in the trophic state of the lake. These genotypic shifts were related to micro-evolutionary changes within the population in their ability to cope with cyanobacteria as food (Hairston et al., 1999, 2001). However, in our single-clone growth experiments we could not confirm our hypothesis that an adaptation of the D. magna population to cyanobacteria in the AaW occurred over the seasons; clones isolated in fall 2008 did not grow better on mixtures with M. aeruginosa than clones isolated in spring did.

Gustafsson et al. (2005) have shown that D. magna can acquire a tolerance to cyanobacteria within a single animal’s lifetime and transfer this tolerance through maternal effects to the next generation. Such a maternal transfer is not accounted for in the single-clone growth assays described above. Maternal effects are adequately considered in multi-generational competition experiments. Such experiments have demonstrated that food quality determined which of the several clones from the D. pulex species complex becomes dominant in an experimental population (Weider et al., 2005, 2008). Here, we conducted such a multi-generational competition experiment, and 10% of the chymotrypsin-inhibitor-producing cyanobacterium significantly affected the clonal composition of the populations. In the competition experiment, the same D. magna clones as used in the single-clone growth assays were cultured together for 90 days on either pure Chlamydomonas sp. or on a mixture of Chlamydomonas sp. and M. aeruginosa at saturating food levels. Two kinds of effects occurred at the end of the experiment: First, the relative frequency of two clones (S2, F11) decreased significantly, whereas three clones (F24, S2, S15) dominated the clonal compositions in both treatments. This is reflected in a reduction of Shannon’s diversity (data not shown). Earlier studies have already demonstrated that the genetic diversity in a multi-clonal competition experiment decreased over time under constant conditions (Nelson et al., 2005; Weider et al., 2008). The single-clone growth experiments served hereby as controls for the competition experiment, as the two experiments were started simultaneously with the same cohort of newborns. Due to the fact that all D. magna clones had positive population growth rates on both treatments, we can rule out the possibility that the decrease in frequency of clone S2 and F11 resulted from a general intolerance to the different food treatments directly from the start.

The second effect observed in the competition experiment was the significantly higher frequency of D. magna clone S7 in the treatment with 10% M. aeruginosa than on the pure green alga Chlamydomonas sp. This suggests that clone S7 has a greater potential to dominate the D. magna population in the presence of M. aeruginosa than in its absence. In the single-clone growth experiment, the somatic and population growth of clone S7 in the presence of M. aeruginosa did not differ from the growth of the other clones. However, the present study underlines earlier results (Weider et al., 2008) showing that single-clone experiments cannot predict the outcome of a complex competition experiment. The discrepancy in growth rates between single clone and competition experiments of D. magna clone S7 can be explained by inducible mechanisms. Increased tolerance can be transferred to the offspring via maternal effects, a fact that has been interpreted as an inducible defense (Gustafsson et al., 2005). It remains unclear whether this inducible tolerance results from microevolution and hence can only be found in D. magna clones originating from lakes containing cyanobacteria.

We hypothesized that D. magna clones from the fall population would show higher clonal frequencies in the M. aeruginosa treatment. This could not be confirmed, and thus we could not find any evidence that the D. magna clones isolated in fall were better adapted to cyanobacteria containing protease inhibitors than the spring clones were. We used three distinct clones of each sample, a number which might not have been sufficient to represent the clonal D. magna composition of the spring and the fall samples in the AaW. Possibly, existing seasonal patterns of tolerance to cyanobacteria might be detectable using a greater number of D. magna clones of each sample. In contrast to the study of Weider et al. (2008) in which seven Daphnia clones from different lakes was used, we used six D. magna clones originating from just one habitat. It is reasonable to assume that clones originating from the same population are genetically more similar with respect to traits that mediate tolerance to cyanobacteria than clones isolated from various populations that differ with respect to the prevalence of cyanobacteria. Sarnelle and Wilson (2005) demonstrated differences in the tolerance to cyanobacteria of D. magna clones from different habitats, giving evidence for local adaptation to cyanobacteria. In populations coexisting with cyanobacteria, less tolerant clones might have become extinct due to natural selection caused by cyanobacteria. Since the selection pressure exerted by protease inhibitors probably already existed over several previous seasons in the AaW, sensitive genotypes would have been under negative selection for numerous generations and thus only be present in low numbers. It remains unclear how many of the D. magna genotypes of the spring population originated from clones which remained in the lake during the winter or originated from hatched ephippia in spring. Ex-ephippia genotypes constitute new genotypes resulting from sexual reproduction and are not under selection by food quality until hatching. D. magna clones freshly hatched from ephippia might thus be less tolerant to cyanobacteria. A high proportion of freshly hatched genotypes from ephippia in the spring should result in a detectably lower fitness on cyanobacterial diet compared to clones from the fall. If more clones of the population in spring originated from D. magna genotypes which had persisted over the winter, the mean selection pressure on the population due to seasonally peaking protease inhibitors could be too small to cause detectable fitness differences between spring and fall samples.

The present study shows for the first time seasonal changes in the potential of a natural lake seston to inhibit the major digestive proteases trypsin and chymotrypsin of D. magna. The inhibitory potential on chymotrypsins was clearly higher in fall than in spring for each of two subsequent years. However, in single-clone growth experiments and competition experiments we could not find seasonal differences in tolerance between the D. magna spring and fall samples to a cyanobacterium containing chymotrypsin inhibitors. Contrary to our hypothesis, D. magna genotypes from the fall were not better adapted to a cyanobacterial diet containing protease inhibitors than genotypes from the spring were. It remains to be seen if the coexistence with seasonally varying dietary protease inhibitors in the AaW has led to a locally adapted D. magna population with such a high level of tolerance to protease inhibitors that seasonally peaking protease inhibitors have no detectable effects on the tolerance of D. magna within this population. It will be interesting to see if a comparison among D. magna populations that exist in the presence or the absence of dietary protease inhibitors provides evidence that seasonally occurring protease inhibitors constitute a constraint strong enough leading to locally adapted populations.

Notes

Acknowledgments

We thank Christoph Effertz and Gesa Heinichen for their excellent help in conducting the experiments, Larry Weider for useful hints with regard to the competition experiment, Hanne Krisch for technical assistance, and Frederic Bartlett for the linguistic improvement of the manuscript. This study was supported by two grants to E. v. E. from the German Research Foundation (DFG): [Grant Number El 179/6-1] and by a grant within the Collaborative Research Centre 680 Molecular Basis of Evolutionary Innovations.

Supplementary material

10750_2012_1303_MOESM1_ESM.eps (1.3 mb)
Fig. 4In vitro effects of increasing concentrations of extracted biomass of each seston sample taken in 2007 on the normalized activity of chymotrypsins of D. magna clone B. IC50 values (which indicate the inhibitory potential of a seston sample and are presented in Fig. 1a as black circles) are based on sigmoid dose-response curves (solid lines). IC50 values with corresponding 95 % confidence intervals (CI) were calculated using the software GraphPad Prism (GraphPad Software, Inc.). Note different scales of the axes. (EPS 1327 kb)
10750_2012_1303_MOESM2_ESM.eps (2.1 mb)
Fig. 5In vitro effects of increasing concentrations of extracted biomass of each seston sample taken in 2008 on the normalized activity of chymotrypsins of D. magna clone B. IC50 values (which indicate the inhibitory potential of a seston sample and are presented in Fig. 1a as black circles) are based on sigmoid dose-response curves (solid lines). IC50 values with corresponding 95 % confidence intervals (CI) were calculated using the software GraphPad Prism (GraphPad Software, Inc.). Note different scales of the axes. (EPS 2120 kb)
10750_2012_1303_MOESM3_ESM.eps (1.3 mb)
Fig. 6In vitro effects of increasing concentrations of extracted biomass of each seston sample taken in 2007 on the normalized activity of trypsins of D. magna clone B. IC50 values (which indicate the inhibitory potential of a seston sample and are presented in Fig. 1a as black circles) are based on sigmoid dose-response curves (solid lines). IC50 values with corresponding 95 % confidence intervals (CI) were calculated using the software GraphPad Prism (GraphPad Software, Inc.). Note different scales of the axes. (EPS 1286 kb)
10750_2012_1303_MOESM4_ESM.eps (2 mb)
Fig. 7In vitro effects of increasing concentrations of extracted biomass of each seston sample taken in 2008 on the normalized activity of trypsins of D. magna clone B. IC50 values (which indicate the inhibitory potential of a seston sample and are presented in Fig. 1a as black circles) are based on sigmoid dose-response curves (solid lines). IC50 values with corresponding 95 % confidence intervals (CI) were calculated using the software GraphPad Prism (GraphPad Software, Inc.). Note different scales of the axes. (EPS 2070 kb)

References

  1. Agrawal, M. K., D. Bagchi & S. N. Bagchi, 2001. Acute inhibition of protease and suppression of growth in zooplankter, Moina macrocopa, by Microcystis blooms collected in Central India. Hydrobiologia 464: 37–44.CrossRefGoogle Scholar
  2. Agrawal, M. K., A. Zitt, D. Bagchi, J. Weckesser, S. N. Bagchi & E. Von Elert, 2005. Characterization of proteases in guts of Daphnia magna and their inhibition by Microcystis aeruginosa PCC 7806. Environmental Toxicology 20: 314–322.PubMedCrossRefGoogle Scholar
  3. Blom, J. F., H. I. Baumann, G. A. Codd & F. Jüttner, 2006. Sensitivity and adaptation of aquatic organisms to oscillapeptin J and [D-Asp(3),(E)-Dhb(7)]microcystin-RR. Archiv für Hydrobiologie 167: 547–559.CrossRefGoogle Scholar
  4. Carlson, R. E., 1977. Trophic State Index for Lakes. Limnology and Oceanography 22: 361–369.CrossRefGoogle Scholar
  5. Colbourne, J. K., B. Robison, K. Bogart & M. Lynch, 2004. Five hundred and twenty-eight microsatellite markers for ecological genomic investigations using Daphnia. Molecular Ecology Notes 4: 485–490.CrossRefGoogle Scholar
  6. Correll, D. L., 1998. The role of phosphorus in the eutrophication of receiving waters: a review. Journal of Environmental Quality 27: 261–266.CrossRefGoogle Scholar
  7. Czarnecki, O., M. Henning, I. Lippert & M. Welker, 2006. Identification of peptide metabolites of Microcystis (Cyanobacteria) that inhibit trypsin-like activity in planktonic herbivorous Daphnia (Cladocera). Environmental Microbiology 8: 77–87.PubMedCrossRefGoogle Scholar
  8. Daniel, T. C., A. N. Sharpley & J. L. Lemunyon, 1998. Agricultural phosphorus and eutrophication: a symposium overview. Journal of Environmental Quality 27: 251–257.CrossRefGoogle Scholar
  9. Dokulil, M. T. & K. Teubner, 2000. Cyanobacterial dominance in lakes. Hydrobiologia 438: 1–12.CrossRefGoogle Scholar
  10. Excoffier, L., G. Laval & S. Schneider, 2005. Arlequin (version 3.0): an integrated software package for population genetics data analysis. Evolutionary Bioinformatics 1: 47–50.Google Scholar
  11. Gademann, K. & C. Portmann, 2008. Secondary metabolites from cyanobacteria: complex structures and powerful bioactivities. Current Organic Chemistry 12: 326–341.CrossRefGoogle Scholar
  12. Gophen, M. & W. Geller, 1984. Filter Mesh Size and Food Particle Uptake by Daphnia. Oecologia 64: 408–412.CrossRefGoogle Scholar
  13. Guo, S. W. & E. A. Thompson, 1992. Performing the exact test of Hardy–Weinberg proportion for multiple alleles. Biometrics 48: 361–372.PubMedCrossRefGoogle Scholar
  14. Gustafsson, S. & L. A. Hansson, 2004. Development of tolerance against toxic cyanobacteria in Daphnia. Aquatic Ecology 38: 37–44.CrossRefGoogle Scholar
  15. Gustafsson, S., K. Rengefors & L. A. Hansson, 2005. Increased consumer fitness following transfer of toxin tolerance to offspring via maternal effects. Ecology 86: 2561–2567.CrossRefGoogle Scholar
  16. Hairston, N. G., W. Lampert, C. E. Caceres, C. L. Holtmeier, L. J. Weider, U. Gaedke, J. M. Fischer, J. A. Fox & D. M. Post, 1999. Lake ecosystems – rapid evolution revealed by dormant eggs. Nature 401: 446.CrossRefGoogle Scholar
  17. Hairston, N. G., C. L. Holtmeier, W. Lampert, L. J. Weider, D. M. Post, J. M. Fischer, C. E. Caceres, J. A. Fox & U. Gaedke, 2001. Natural selection for grazer resistance to toxic cyanobacteria: evolution of phenotypic plasticity? Evolution 55: 2203–2214.PubMedGoogle Scholar
  18. Hansson, L. A., S. Gustafsson, K. Rengefors & L. Bomark, 2007. Cyanobacterial chemical warfare affects zooplankton community composition. Freshwater Biology 52: 1290–1301.CrossRefGoogle Scholar
  19. Hessen, D. O., 1985. Filtering structures and particle-size selection in coexisting Cladocera. Oecologia 66: 368–372.CrossRefGoogle Scholar
  20. Johnk, K. D., J. Huisman, J. Sharples, B. Sommeijer, P. M. Visser & J. M. Stroom, 2008. Summer heatwaves promote blooms of harmful cyanobacteria. Global Change Biology 14: 495–512.CrossRefGoogle Scholar
  21. Johnston, K. A., M. J. Lee, C. Brough, V. A. Hilder, A. M. R. Gatehouse & J. A. Gatehouse, 1995. Protease activities in the larval midgut of Heliothis virescens:evidence for trypsin and chymotrypsin-like enzymes. Insect Biochemistry and Molecular Biology 25: 375–383.CrossRefGoogle Scholar
  22. Lampert, W. & K. O. Rothhaupt, 1991. Alternating dynamics of Rotifers and Daphnia-Magna in a shallow lake. Archiv für Hydrobiologie 120: 447–456.Google Scholar
  23. Long, B. M., G. J. Jones & P. T. Orr, 2001. Cellular microcystin content in N-limited Microcystis aeruginosa can be predicted from growth rate. Applied and Environmental Microbiology 67: 278–283.PubMedCrossRefGoogle Scholar
  24. Lürling, M., 2003. Daphnia growth on microcystin-producing and microcystin-free Microcystis aeruginosa in different mixtures with the green alga Scenedesmus obliquus. Limnology and Oceanography 48: 2214–2220.CrossRefGoogle Scholar
  25. Lürling, M. & E. van der Grinten, 2003. Life-history characteristics of Daphnia exposed to dissolved microcystin-LR and to the cyanobacterium Microcystis aeruginosa with and without microcystins. Environmental Toxicology and Chemistry 22: 1281–1287.PubMedGoogle Scholar
  26. Lynch, M., 1987. The consequences of fluctuating selection for isozyme polymorphisms in Daphnia. Genetics 115: 657–669.PubMedGoogle Scholar
  27. Martin-Creuzburg, D. & E. Von Elert, 2004. Impact of 10 dietary sterols on growth and reproduction of Daphnia galeata. Journal of Chemical Ecology 30: 483–500.PubMedCrossRefGoogle Scholar
  28. Martin-Creuzburg, D., E. Von Elert & K. H. Hoffmann, 2008. Nutritional constraints at the cyanobacteria-Daphnia magna interface: the role of sterols. Limnology and Oceanography 53: 456–468.CrossRefGoogle Scholar
  29. Moran, M. D., 2003. Arguments for rejecting the sequential Bonferroni in ecological studies. Oikos 100: 403–405.CrossRefGoogle Scholar
  30. Nelson, W. A., E. McCauley & F. J. Wrona, 2005. Stage-structured cycles promote genetic diversity in a predator-prey system of Daphnia and algae. Nature 433: 413–417.PubMedCrossRefGoogle Scholar
  31. Ortells, R., C. Olmo & X. Armengol, 2012. Colonization in action: genetic characteristics of Daphnia magna Strauss (Crustacea, Anomopoda) in two recently restored ponds. Hydrobiologia 689: 37–49.CrossRefGoogle Scholar
  32. Porter, K. G., J. Gerritsen & J. D. Orcutt, 1982. The effect of food concentration on swimming patterns, feeding-behavior, ingestion, assimilation, and respiration by Daphnia. Limnology and Oceanography 27: 935–949.CrossRefGoogle Scholar
  33. Porter, K. G. & R. Mcdonough, 1984. The energetic cost of response to blue-green-algal filaments by Cladocerans. Limnology and Oceanography 29: 365–369.CrossRefGoogle Scholar
  34. Reynolds, J., B. S. Weir & C. C. Cockerham, 1983. Estimation of the co-ancestry coefficient – basis for a short-term genetic-distance. Genetics 105: 767–779.PubMedGoogle Scholar
  35. Rohrlack, T., E. Dittmann, T. Börner & K. Christoffersen, 2001. Effects of cell-bound microcystins on survival and feeding of Daphnia spp. Applied & Environmental Microbiology 67: 3523–3529.CrossRefGoogle Scholar
  36. Rohrlack, T., K. Christoffersen, P. E. Hansen, W. Zhang, O. Czarnecki, M. Henning, J. Fastner, M. Erhard, B. A. Neilan & M. Kaebernick, 2003. Isolation, characterization, and quantitative analysis of microviridin J, a new Microcystis metabolite toxic to Daphnia. Journal of Chemical Ecology 29: 1757–1770.PubMedCrossRefGoogle Scholar
  37. Sarnelle, O. & A. E. Wilson, 2005. Local adaptation of Daphnia pulicaria to toxic cyanobacteria. Limnology and Oceanography 50: 1565–1570.CrossRefGoogle Scholar
  38. Schreurs, H., 1992. Cyanobacterial dominance, relation to eutrophication and lake morphology, Thesis, University of Amsterdam.Google Scholar
  39. Schwarzenberger, A., A. Zitt, P. Kroth, S. Mueller & E. von Elert, 2010. Gene expression and activity of digestive proteases in Daphnia: effects of cyanobacterial protease inhibitors. BMC physiology 10: 6.PubMedCrossRefGoogle Scholar
  40. Sivonen, K.& G. Jones, 1999. Cyanobacterial toxins. Toxic Cyanobacteria in Water: A Guide to Their Public Health Consequences, Monitoring, and Management: 41–111.Google Scholar
  41. Sommer, U., Z. M. Gliwicz, W. Lampert & A. Duncan, 1986. The Peg-model of seasonal succession of planktonic events in fresh waters. Archiv für Hydrobiologie 106: 433–471.Google Scholar
  42. Threlkeld, S. T., 1979. Midsummer dynamics of 2 Daphnia species in Wintergreen Lake, Michigan. Ecology 60: 165–179.CrossRefGoogle Scholar
  43. Vanoverbeke, J. & L. De Meester, 2010. Clonal erosion and genetic drift in cyclical parthenogens – the interplay between neutral and selective processes. Journal of Evolutionary Biology 23: 997–1012.PubMedCrossRefGoogle Scholar
  44. Von Elert, E. & F. Jüttner, 1997. Phosphorus limitation and not light controls the extracellular release of allelopathic compounds by Trichormus doliolum (cyanobacteria). Limnology and Oceanography 42: 1796–1802.CrossRefGoogle Scholar
  45. Von Elert, E. & T. Wolffrom, 2001. Supplementation of cyanobacterial food with polyunsaturated fatty acids does not improve growth of Daphnia. Limnology and Oceanography 46: 1552–1558.CrossRefGoogle Scholar
  46. Von Elert, E., 2002. Determination of limiting polyunsaturated fatty acids in Daphnia galeata using a new method to enrich food algae with single fatty acids. Limnology and Oceanography 47: 1764–1773.CrossRefGoogle Scholar
  47. Von Elert, E., D. Martin-Creuzburg & J. R. Le Coz, 2003. Absence of sterols constrains carbon transfer between cyanobacteria and a freshwater herbivore (Daphnia galeata). Proceedings of the Royal Society of London Series B-Biological Sciences 270: 1209–1214.CrossRefGoogle Scholar
  48. Von Elert, E., M. K. Agrawal, C. Gebauer, H. Jaensch, U. Bauer & A. Zitt, 2004. Protease activity in gut Daphnia magna: evidence for trypsin and chymotrypsin enzymes. Comparative Biochemistry and Physiology B-Biochemistry & Molecular Biology 137: 287–296.CrossRefGoogle Scholar
  49. Von Elert, E., L. Oberer, P. Merkel, T. Huhn & J. F. Blom, 2005. Cyanopeptolin 954, a chlorine-containing chymotrypsin inhibitor of Microcystis aeruginosa NIVA Cya 43. Journal of Natural Products 68: 1324–1327.CrossRefGoogle Scholar
  50. Von Elert, E., A. Zitt & A. Schwarzenberger A, 2012. Inducible tolerance in Daphnia magna against dietary protease inhibitors. Journal of Experimental Biology [accepted].Google Scholar
  51. Wacker, A. & E. Von Elert, 2001. Polyunsaturated fatty acids: evidence for non-substitutable biochemical resources in Daphnia galeata. Ecology 82: 2507–2520.Google Scholar
  52. Weider, L. J., W. Lampert, M. Wessels, J. K. Colbourne & P. Limburg, 1997. Long-term genetic shifts in a microcrustacean egg bank associated with anthropogenic changes in the Lake Constance ecosystem. Proceedings of the Royal Society of London – Series B: Biological Sciences 264: 1613–1618.CrossRefGoogle Scholar
  53. Weider, L. J., W. Makino, K. Acharya, K. L. Glenn, M. Kyle, J. Urabe & J. J. Elser, 2005. Genotype x environment interactions, stoichiometric food quality effects, and clonal coexistence in Daphnia pulex. Oecologia 143: 537–547.PubMedCrossRefGoogle Scholar
  54. Weider, L. J., P. D. Jeyasingh & K. G. Looper, 2008. Stoichiometric differences in food quality: impacts on genetic diversity and the coexistence of aquatic herbivores in a Daphnia hybrid complex. Oecologia 158: 47–55.PubMedCrossRefGoogle Scholar
  55. Wiedner, C., P. M. Visser, J. Fastner, J. S. Metcalf, G. A. Codd & L. R. Mur, 2003. Effects of light on the microcystin content of Microcystis strain PCC 7806. Applied and Environmental Microbiology 69: 1475–1481.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Christian J. Kuster
    • 1
  • Anke Schwarzenberger
    • 1
  • Eric von Elert
    • 1
  1. 1.Zoological Institute, Aquatic Chemical EcologyUniversity of CologneCologneGermany

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