Collapse of a Planktothrix agardhii perennial bloom and microcystin dynamics in response to reduced phosphate concentrations in a temperate lake

R E S E A R C H A R T I C L E

CollapseofaPlanktothrixagardhii perennial bloomandmicrocystindynamics in response to reduced phosphate concentrations inatemperate lakeArnaud Catherine1, Catherine Quiblier1,2, Claude Yepremian1, Patrice Got3, Alexis Groleau4,Brigitte Vincon-Leite5, Cecile Bernard1 & Marc Troussellier3

1USM 505/EA 4105 ‘Ecosystemes et interactions toxiques’, Museum National d’Histoire Naturelle, Paris, France; 2Universite Paris 7, Paris, France; 3UMR-

CNRS 5119 ‘Laboratoire Ecosystemes Lagunaires’, Universite Montpellier II, Montpellier, France; 4UMR-CNRS 7154 IPGP, Universite Paris 7 and IPGP,

Paris, France; and 5Centre d’Enseignement et de Recherche sur l’Eau, la Ville et l’Environnement (CEREVE), Ecole Nationale des Ponts et Chaussees,

Marne-la-Vallee, France

Correspondence: Cecile Bernard, USM 505/

EA 4105 Ecosystemes et interactions

toxiques, Museum National d’Histoire

Naturelle, 57, rue Cuvier, Case 39, 75231

Paris, Cedex 05, France. Tel.: 133 1 40 79 31

83; fax: 133 1 40 79 35 94; e-mail:

[email protected]

Received 20 July 2007; revised 6 March 2008;

accepted 15 March 2008.

First published online 6 May 2008.

DOI:10.1111/j.1574-6941.2008.00494.x

Editor: Riks Laanbroek

Keywords

Planktothrix agardhii ; pico- and

nanophytoplankton; shallow lake; statistical

modeling; microcystin.

Abstract

Planktothrix agardhii dynamics, microcystin concentration and limnological

variables were monitored every 2 weeks for 2 years (2004–2006) in a shallow

hypereutrophic artificial lake (BNV, Viry-Chatillon, France). Time-series analysis

identified two components in the P. agardhii biomass dynamics: (1) a significant

decreasing trend in P. agardhii biomass (65% of the overall variance) and (2) a

residual component without significant seasonal periodicity. A path-analysis

model was built to determine the main factors controlling the P. agardhii dynamics

over the period studied. The model explained 66% of P. agardhii biomass changes.

The decreasing trend in P. agardhii biomass was significantly related to a decrease

in the PO43� concentration resulting from an improved treatment of the incoming

watershed surface water. The residual component was related to zooplankton

dynamics (cyclopoid abundances), supporting the hypothesis of a top-down

control of P. agardhii, but only when the biomass was low. Forty-nine percent of

the variability in the microcystin (MC) concentration (min:o 0.1 mg equiva-

lent MC-LR L�1; max: 7.4mg equivalent MC-LR L�1) could be explained by changes

in the P. agardhii biomass. The highest toxin content was observed when

P. agardhii biomass was the lowest, which suggests changes in the proportion of

microcystin-producing and -nonproducing subpopulations and/or the physiolo-

gical status of cells.

Introduction

Cyanobacterial blooms cause health and environmental

concerns (Chorus, 2005). Planktothrix agardhii appears to

be one of the most potentially toxic cyanobacterial species

in European waters (Noges & Ott, 2003; Willame et al.,

2005). A recent study of health risks linked to the presence

of cyanobacteria in French surface waters (Levi et al.,

2006) showed that P. agardhii was one of the most

frequently encountered genera (46% of 1699 samples).

Planktothrix agardhii blooms were often associated with

the detection of large amounts of microcystin (MC) and

toxicity in various biological models (for a review, see

Wiegand & Pflugmacher, 2005). The effects of P. agardhii

toxic blooms on human health were shown by Codd

et al. (1999).

The increase of the occurrence, dominance and bloom of

potentially toxic cyanobacteria in the continental waters has

been recognized as a direct consequence of the eutrophica-

tion of these ecosystems. Over the past 10 years, it has been

shown that efforts to reduce eutrophication (oligotrophica-

tion) of continental aquatic ecosystems could be successful

in reducing noxious cyanobacterial populations, such as

P. agardhii (Keto & Tallberg, 2000; Kangro et al., 2005;

Kohler et al., 2005).

The multifactorial control of P. agardhii dynamics and

toxicity has been evidenced by various in vitro studies on

P. agardhii strains. They mainly focused on the effects of

FEMS Microbiol Ecol 65 (2008) 61–73 c� 2008 Federation of European Microbiological SocietiesPublished by Blackwell Publishing Ltd. All rights reserved

nutrients (e.g. Ahlgren, 1977; Sivonen, 1990; Znachor et al.,

2006), temperature (Robarts & Zohary, 1987; Sivonen,

1990), light (Sivonen, 1990) and zooplankton (e.g. Davido-

wicz et al., 1988; Weithoff & Walz, 1995). While in vitro

studies may help to determine the ecophysiological or

ecotoxicological characteristics of the strains studied and

allow comparing them with other species, only in situ

approaches can show the relative effects of multifactorial

control on natural populations. However, although a large

number of field surveys of P. agardhii have been carried out

and limnological characteristics of different lakes have been

determined, very few studies used numerical analysis, such

as statistical or dynamic models, to detect and quantify the

relationships between P. agardhii and environmental vari-

ables. The dynamic simulation model of Oscillatoria agard-

hii in Lake Vechten proposed by Montealegre et al. (1995)

appears to be the only example of modeling P. agardhii

dynamics in situ. Statistical models were recently shown to

be very useful for linking both Microcystis and Anabaena

biomass dynamics, and microcystin concentrations to en-

vironmental changes in Quebec lakes (Rolland et al., 2005).

As P. agardhii can produce microcystins and thus cause

health concerns, numerical analyses are needed to improve

the understanding of the in situ controlling factors of

P. agardhii dynamics in different lakes. It is still unclear

whether the same factors regulate the biomass of P. agardhii

or whether the factors are rather site specific. Finding out

more about these factors and their relative importance is

a first step in drawing up numerical models able to simulate

P. agardhii dynamics and proposing management strategies

to reduce the spread of blooms.

The artificial lake at Viry-Chatillon (BNV) located South

of Paris is used for leisure activities and was shown to be

interesting to further explore both the P. agardhii dynamics

and its links with limnological characteristics and micro-

cystin concentrations (Briand et al., 2002; Yepremian et al.,

2007). These studies showed (1) the strong dominance

of the phytoplankton community by P. agardhii, (2) the

associated in situ microcystin concentrations, and (3) the

diversity of microcystin variants produced by strains iso-

lated from this ecosystem. A survey was carried out every 2

weeks between 2004 and 2006 to analyze and model the

dynamics of P. agardhii in this ecosystem. Conventional

time-series analysis and path analysis were used. These

statistical analyses are an intermediate approach between

dynamics and multiple regression modeling (Legendre &

Legendre, 1998). Simultaneous related changes in other

phytoplanktonic groups (pico- and nanophytoplankton)

were also studied. Finally, P. agardhii dynamics, microcystin

concentrations and cellular microcystin content were com-

pared to estimate the usefulness of the P. agardhii biomass

as a predictor of microcystin concentration in the BNV

ecosystem.

Materials and methods

Study site and sampling procedure

This study was carried out in the ‘Base Nautique de Viry-

Chatillon’ (BNV), a water sports center located

(2123004.200E, 48140003.300N) in the south suburban area of

Paris (France). It is a shallow artificial lake (mean depth:

2.8 m; 98 ha) that receives both diffuse and point source

inputs from an 810 ha watershed. The main point source

input is a rainfall collector, located on the south shore of the

lake collecting untreated surface water from a 430 ha sub-

basin. In 2003, an in-line treatment plant was added to the

rainfall collector to improve the quality of the outflow water

released into the BNV. This comprised filtration (3 mm),

flotation and decantation to limit the discharge of sus-

pended solids.

The sampling campaign was carried out every 2 weeks

between March 23, 2004 and March 21, 2006. Three

replicate samples were taken 0.5 m below the water surface

at a distance of c. 3 m.

Meteorological variables

Data were obtained for total solar radiation (average

between two sampling date) and wind speed at 10-m height

(averaged over the 72 h before sampling) from the Orly

airport meteorological station (Meteo-France), 6 km far

from the study site.

Physical and chemical variables

Water temperature, dissolved oxygen andconductivity

Water temperature was measured every 10 min using an in

situ thermistor chain (four SEAMON-MINI electronic tem-

perature recorders, accuracy 0.05 1C, Hugrun Inc., Reykja-

vik, Iceland). The average surface temperature over the

period between two sampling dates was then calculated.

Dissolved oxygen and conductivity were measured at

each sampling date using a multiparameter Sea-Bird SBE

19 Seacat Profiler (Sea-Bird Electronics Inc., WA).

Chemical analysis

The samples used to quantify dissolved nutrients were

immediately filtered using a cellulose acetate 0.22-mm syr-

inge filter (Nalgene, Rochester, NY). Ammonium (NH41)

and orthophosphate (PO43�) analyses were carried out on the

day of sampling using a spectrophotometric method as

described previously (Greenberg et al., 1999). The detection

limits were 1 mM for NH41 and 0.1 mM for PO4

3�. The nitrate

(NO3�) concentration was measured using a DX600 ion

FEMS Microbiol Ecol 65 (2008) 61–73c� 2008 Federation of European Microbiological SocietiesPublished by Blackwell Publishing Ltd. All rights reserved

62 A. Catherine et al.

chromatograph (Dionex Corp., Westmont, IL) equipped

with an AS14 IonPack analytical column (Dionex Corp.).

The detection limit was 0.85 mM.

Raw water was also collected in acid-washed polyethylene

containers for total phosphorus (TP) analysis using a

spectrophotometric method following acid digestion as

described previously (AFNOR, 1982), allowing a detection

limit of 0.4mM.

Biological variables

Chlorophyll a (Chl a ) concentration

The Chl a concentration was measured by filtering 300 mL

of water (Whatman GF-C filters), followed by methanol

extraction (Talling & Driver, 1963) and analysis with a Cary

50 Scan spectrophotometer (Varian Inc., Palo Alto).

Phytoplankton communities

The microphytoplankton species (420 mm) were deter-

mined using samples fixed in buffered formaldehyde (5%

v/v for high biomass species) or Lugol (for low biomass

species) as described previously (Yepremian et al., 2007).

The microphytoplankton units were counted using a Malas-

sez counting chamber with a Nikon Optiphot 2 microscope

(� 400, Nikon, Melville) or a Utermohl chamber (Lund,

1951) with an Olympus CK2 inverted microscope (� 400,

Olympus Optical Co., Tokyo, Japan) for low phytoplankton

biomass. At least 400 phytoplankton units (cells, trichomes,

colonies) were counted to reduce the estimation error to

o 10%. The biovolumes of each species were estimated as

described by Sun & Liu (2003). The wet weight biomass was

calculated from biovolume estimates, assuming that the

phytoplankton cells had a density of 1 g cm�3.

Nano- (2–20 mm) and picophytoplankton (� 2 mm) cells

were detected and counted by flow cytometry as described

previously (Troussellier et al., 1993; Campbell et al., 1994;

Crosbie et al., 2003). Analyses were performed using a

FACSCalibur flow cytometer (Becton Dickinson, San Jose,

CA) with an air-cooled argon laser (488 nm, 15 mW).

Subsamples were fixed with buffered formalin (2.5% v/v)

and stored immediately in liquid nitrogen until analysis.

Cells excited at 488 nm were detected and counted using

their right-angle light scattering (RALS) properties and their

orange (585/42 nm filter) and red (4650 nm filter) fluores-

cence from phycoerythrin and chlorophyll pigments, respec-

tively. For each analysis, fluorescent beads (1, 2, 6, 10, 20mm,

Polysciences Inc., Warrington, PA) were systematically

added to each sample to standardize the flow cytometer

settings. Cell abundances were estimated by adding a known

volume of fluorescent beads (True-Counts, Becton Dickin-

son) with known concentration.

Zooplankton communities

Zooplankton was collected by filtering 1.5 L of water

through a 60mm mesh and then preserved in a 70%

ethanol solution (Cottenie et al., 2003). Abundances (in-

dividuals L�1) were estimated using an inverted microscope

(Olympus CK2, Olympus Optical Co.) after sample sedi-

mentation. Five major taxonomic groups were distinguished:

rotifers, copepod nauplii (NAC), cyclopoid copepods (CYC),

calanoids copepods and cladocerans (CLA).

Toxicity assays

The cellular microcystin concentrations of the field samples

were estimated using the protein phosphatase 2A inhibition

assay (PP2A) as described previously (Briand et al., 2002).

The results are expressed in micrograms equivalent of MC-

LR L�1. The detection limit was 0.1 mg equivalent MC-

LR L�1 of raw water.

Data analyses

The following successive steps were followed: (1) character-

ization of the components of P. agardhii biomass dynamics

(time-series analyses), (2) identification of the P. agardhii

biomass-controlling factors among the different hypothe-

sized ones (path-analysis modeling) and (3) identification of

the dynamics components-specific controlling factors (cor-

relation analysis between the identified controlling factors

and the components of P. agardhii dynamics).

Time-series analyses were carried out on the 2004–2006

series to identify (1) trends and (2) periodicity, using a linear

regression model and contingency periodogram analysis

(Legendre et al., 1981), respectively.

To identify the P. agardhii biomass-controlling factors, a

path-analysis model (Wright, 1921) was built on the basis of

current knowledge about factors controlling phytoplankton

in lakes, including bottom-up, top-down and physical

forcing variables (Fig. 1a). The model underlying the path

diagram of Fig. 1a was obtained by refining our initial

guesses about the potential interrelations between P. agard-

hii biomass and the other measured variables. For instance,

both NH41 and NO3

� have first been introduced as potential

N sources controlling P. agardhii biomasses; only NH41 was

significantly related to P. agardhii, and thus retained in the

model. Then, in this study, we chose to use PO43� and not TP

as an explanatory variable in the path-analysis model. In our

case, TP can be considered to be a proxy of phytoplankton

biomass as shown by the high correlation coefficient

(r = 0.91, Po 10�4) that can be computed from the dataset.

In addition to this, the expected lack of independency

between these two variables does not allow including TP as

an explanatory variable in a statistical model such as the one

used in this study.

FEMS Microbiol Ecol 65 (2008) 61–73 c� 2008 Federation of European Microbiological SocietiesPublished by Blackwell Publishing Ltd. All rights reserved

63P. agardhii bloom collapse and microcystin dynamics

The considered bottom-up factors were the concentration

of nutrients (NH41, PO4

3�) and light (L). Abundances of

NAC, CYC and CLA were considered to be top-down

variables. Rotifers were not included in the model as their

small size precluded grazing activity on P. agardhii tri-

chomes. Calanoid copepods were not considered due to

their scarce abundance (o10 individuals L�1).

Water temperature (T ) and wind (W), which act on water

column turbulence, were considered to have a potential

direct effect on P. agardhii biomass as well as an indirect

effect through zooplankton variables.

The Kolmogorov–Smirnov test of goodness of fit, as

modified by Lilliefors (1967), was used to test whether the

frequency distribution of the variables was normal. When

the distribution was not normal, the Box–Cox method

(Sokal & Rohlf, 1981) was used to search for the best

normalizing transformation. A logarithmic transformation

was applied to the NAC, CYC, CLA, NH41, PO4

3� and L

variables.

The linearity of the relationships between variable pairs

was then checked by comparing the significance values in

Pearson’s r correlation matrices between variables with those

found using Kendall’s t matrices. Finally, all variables were

normalized (subtracting the mean to each value and divid-

ing by the SD) to remove the effect of the measurement scale

of each variable and eliminate the constants in the path

analysis equations.

Multicollinearity due to correlation between explanatory

variables was checked and, if necessary, taken into account

to estimate path coefficients using a ridge regression proce-

dure (Hocking, 1976).

Basic statistics as well as linear or nonlinear models were

calculated using STATVIEW software (SAS Institute Inc., NC).

Kolmogorov–Smirnov and Box–Cox tests were performed

using the R package (Casgrain & Legendre, 2004). Path

analysis was performed using PISTE (version 3.1.2), a specific

software created by A. Vaudor (Departement des sciences

biologiques, Universite de Montreal).

Results

Limnological characteristics of the BNV

Table 1 presents the mean and range of the main limnolo-

gical variables recorded during an earlier survey (2001–

2002) (Yepremian et al., 2007) and during this study

(2004–2006) of the BNV.

The only variables that were significantly different be-

tween the two series were PO43� and TP, whose concentra-

tions were lower in 2004–2006 (Table 1). Moreover, PO43�

and TP trends, tested by fitting a linear regression between

observed values and time, were shown to be significantly

decreasing during the 2004–2006 period (PO43�: R2 = 0.534,

Po 10�4, Fig. 2c; TP: R2 = 0.692, Po 10�4).

Vertical temperature profiles did not show any long-term

stratification (thermal stratification Z1 1C m�1 did not last

more than 4 days over the period studied; data not shown)

over the 2004–2006 period.

Planktothrix agardhii and phytoplanktondynamics

The mean values of P. agardhii biomass decreased from

166 mg L�1 in 2001–2002 to 49 mg L�1 in 2004–2006

(Table 1).

CYC

CYC

++

+

−

−+

+

CLANAC

Pa WT

L

NAC

Pa

NH4+

PO43−

NH4+

PO43−

T

L

W

CLA

(b)

(a)

Fig. 1. (a) Path diagram for the proposed general model. Pa, Plankto-

thrix agardhii biomass (variable to be explained); T, temperature; L, solar

radiation; W, wind speed; NH41, ammonium concentration; PO4

3�, ortho-

phosphate concentration; NAC, copepod nauplii abundance; CYC,

cyclopoid copepod abundance; CLA, cladocerans abundance. (b) Sig-

nificant direct paths (P � 0.05, arrows) between variable pairs. Only the

signs of the significant path coefficients are shown here (values are given

in Table 2).

FEMS Microbiol Ecol 65 (2008) 61–73c� 2008 Federation of European Microbiological SocietiesPublished by Blackwell Publishing Ltd. All rights reserved

64 A. Catherine et al.

The dynamics of P. agardhii biomasses observed during

the 2004–2006 study is shown in Fig. 2a. Following a period

of relative stability (from March to December, 2004; mean-

97� 16 mg L�1), there was a collapse in P. agardhii biomass

at the beginning of 2005. After a slight increase during 2006

winter, the biomass decreased again from February 2006.

Further ad hoc field observations did not show any return to

the high level of P. agardhii biomass (A. Catherine, unpub-

lished data). Such low P. agardhii biomass values were never

recorded during the previous study (see the minima values

in Table 1).

Chl a concentration variation showed a pattern similar to

P. agardhii biomass (Fig. 2a). There was a highly significant

linear relationship between Chl a concentration and bio-

mass (R2 = 0.495, Po 10�3). However, only half of the

variation in Chl a concentration (Fig. 2a) can be explained

by variation in P. agardhii biomass.

Planktothrix agardhii was clearly dominant (4 90%) in

all the samples of the 2001–2002 series (Table 1). In the

2004–2006 series, the contribution of P. agardhii to the

total biomass of microphytoplankton was more variable.

At the time of the drastic decrease of P. agardhii (from

January to May 2005), other phytoplankton groups in-

creased. In the first part of this period (from January to

March 2005), the only other microphytoplankton species

detected in significant amount was the filamentous cyano-

bacteria Limnothrix redekei (Fig. 2a). From April 2005

onwards, this species then showed the same strong decline

as P. agardhii. Flow-cytometry analyses outlined that while

the microphytoplankton decreased in abundance, there was

an increase in nano- and picophytoplankton cell abundance

(Fig. 2b): from 8.0� 101 cells mL�1 for picophytoplankton

and 3.3� 101 cells mL�1 for nanophytoplankton to 105 and

104 cells mL�1, respectively (Fig. 2b). This increase also

corresponded to the warm water period in the second

year of the survey (Fig. 2d). Abundances of pico- and

nanophytoplankton returned to low values at the end of

the study period, also corresponding to a decrease of water

temperature.

The P. agardhii biomass decreasing trend, tested by linear

regression between the observed values and time, was shown

to be highly significant (P � 10�3). This linear trend repre-

sented almost 65% of the overall variability of P. agardhii

(Fig. 3). The linear trend was removed from the observed

values to make the residuals stationary. This is a basic

condition for testing the periodicity of a time series. The

contingency periodogram method was used to search for

significant periodicity (P � 0.05) in the residual series.

However, no significant period was detected. Thus, while

P. agardhii biomass was quite variable during the 2-year

survey, neither seasonal nor higher frequency periodic

components were detected.

Planktothrix agardhii dynamics controllingfactors

Figure 1b represents the direct significant paths (P � 0.05)

for the variables included in the model (Fig. 1a). Table 2

shows the partition of the total covariance (column A)

of each variable pair, X and Y, into direct (X ! Y, col-

umn B) and indirect (X ! Z ! Y, i.e. where an indirect

pathway between X and Y through Z can be found in the

model, column C) and explained and residual (column E)

components.

Table 1. Mean, range, standard deviation and coefficient of variation (%) of the main limnological characteristics, Planktothrix agardhii biomass and

% of total biomass, and microcystin concentration as observed during two consecutive studies of the BNV

Variable

2001–2002 2004–2006

Mean Min Max SD CV (%) n Mean Min Max SD CV (%) n

T ( 1C) 15.2 4.3 24.3 6.9 45.9 11 13.6 2.7 26.1 7.1 52.1 52

DO (mg L�1) 10.8 9.3 12.8 1.3 12.0 10 10.8 6.6 14.0 1.8 16.7 36

DO (%) 104.4 88.0 125.0 11.2 10.7 10 95.8 74.4 133.3 15.0 15.7 29

C (mS cm�1) 969.9 933.0 1011.0 24.1 2.5 10 938.3 627.2 1176.0 167.5 17.9 30

NH41 (mM) 1.4 0.3 2.7 0.9 62.6 10 1.8 0.2 7.5 1.7 94.4 52

NO3� (mM) 8.2 oDL 35.9 10.6 128.8 11 8.6 oDL 52.8 13.5 156.9 50

PO43� (mM) 0.6 0.2 1.8 0.5 74.4 10 0.2 oDL 0.7 0.1 70.2 52

TP (mM) 14.6 5.6 27.8 7.8 53.2 10 3.0 0.9 5.8 1.6 52.4 52

Secchi depth (m) 0.4 0.3 0.4 0.04 10.0 10 0.6 0.3 1.6 0.3 45.9 52

Chl a (mg L�1) 63.5 22.0 136.5 31.5 49.6 18 63.0 9.9 159.6 32.2 51.2 52

Pa (mg L�1) 166.3 81.6 605.2 157.9 94.9 10 49.3 0.0 135.1 42.8 86.8 52

Pa (%) 98.0 92.0 100.0 2.9 2.9 10 80.9 23.2 100.0 21.4 26.4 52

MC (mg equ L�1) 9.9 1.6 35.0 8.7 87.6 18 2.2 oDL 7.4 2.0 93.1 52

T, temperature; DO, dissolved oxygen concentration; DO (%), dissolved oxygen saturation; C, conductivity; TP, total phosphorus; Chl a, chlorophyll a

concentration; Pa, P. agardhii biomass; Pa (%), P. agardhii as a percentage of total biomass; MC, microcystin concentration; DL, detection limit.

The 2001–2002 series refers to the study carried out by Yepremian et al. (2007).

FEMS Microbiol Ecol 65 (2008) 61–73 c� 2008 Federation of European Microbiological SocietiesPublished by Blackwell Publishing Ltd. All rights reserved

65P. agardhii bloom collapse and microcystin dynamics

The model explained nearly 66% of the P. agardhii

changes over the 2 consecutive years of this study.

Among the eight variables that were thought to have a

direct action on P. agardhii biomass (Fig. 1a), only three

were found to have a significant direct effect (Table 2 and

Fig. 1b). The values of path coefficients (Table 2, column B)

indicated that PO43� concentration is the most significant

variable (0.564, P � 0.001), followed by cyclopoid abun-

dance (� 0.355, P � 0.05) and NH41 concentration (� 0.303,

P � 0.01). PO43� concentration had a positive influence on

P. agardhii biomass. In contrast, both NH41 concentration

and CYC abundance (Fig. 2d) had a negative effect on P.

agardhii biomass. The other two types of zooplankton did

not show any significant explained covariance with P.

agardhii biomass, although their total covariance with P.

agardhii was significant (Table 2).

No direct effects of wind (W), light irradiance (L) or

temperature (T) were detected (Table 2).

Temperature (T) was found to have a significant effect

on the abundance of the three categories of zooplankton

(Table 2). This parameter alone explained between 30% and

50% of the variability in zooplankton abundances.

(a)

10

100

1000

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

0

25

50

75

100

125

FAJ A

(b)

1E+01

1E+02

1E+03

1E+04

1E+05

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

(c)

0.01

0.1

1

10

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

(d)

0

5

10

15

20

25

30

Tem

pera

ture

(°C

)

0

100

200

300

400

500

600

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

Fig. 2. Changes in the main biotic and abiotic

variables measured during the 2004–2006 study.

(a) Chl a concentration in mg L�1 ( ), Plankto-

thrix agardhii (�) and Limnothrix redekei (�)biomasses in mg L�1. (b) Picophytoplankton (m)

and nanophytoplankton (}) abundances in cells

mL�1. (c) NH41 (~) and PO4

3� (&) concentrations

in mM. (d) Cyclopoid abundance (� ) in indivi-

duals L�1, Temperature (dashed line) in 1C.

FEMS Microbiol Ecol 65 (2008) 61–73c� 2008 Federation of European Microbiological SocietiesPublished by Blackwell Publishing Ltd. All rights reserved

66 A. Catherine et al.

As the P. agardhii dynamics showed a highly significant

linear decreasing trend and a higher frequency component,

the significant explanatory variables were examined to

determine which one could be related to these two different

components of P. agardhii dynamics. Correlations were first

calculated between P. agardhii biomass values deduced from

the linear regression model obtained from the P. agardhii

time series and the values of the three significant explana-

tory variables (CYC, NH41, PO4

3�). A highly significant

relationship (r = 0.825, P � 10�3) was observed only be-

tween P. agardhii biomass and Ln PO43� concentrations (Fig.

3b). The decreasing trend observed in P. agardhii biomass

during the 2004–2006 study was strongly related to a

decrease in the concentration of this inorganic nutrient.

Correlations were also calculated between the residual values

of the P. agardhii series and the significant explanatory

variables. Only CYC abundances were correlated with

P. agardhii residual biomass (r =� 0.622, Po 10�3) (Fig. 3c).

The trend and residual components of the P. agardhii

dynamics were, therefore, correlated with different control-

ling variables. The former was positively correlated with

PO43� concentration and the second was negatively corre-

lated with CYC abundance.

Microcystin concentrations

Microcystin concentrations estimated during this study

ranged between o 0.1 (detection limit) and 7.4 mg equiva-

lent MC-LR L�1 (Fig. 4). These microcystin values were

lower than those observed during the previous study

(Table 1).

The relationship between the microcystin concentration

(y) and P. agardhii biomass (x) was further investigated

using a linear regression model. This model

(y = 0.51210.034x, R2 = 0.49, Po 10�4) explained a signifi-

cant part (49%) of microcystin changes. The remaining

unexplained variance could result in changes in microcystin

content per unit of P. agardhii biomass over the period

studied.

Figure 4 shows the P. agardhii microcystin content varia-

tions (only for periods when there was a reliable value for

the ratio between microcystin concentrations and P. agardhii

biomass). Ratios for low microcystin concentrations and/or

low P. agardhii biomasses were not calculated (from March

to October 2005) as such low values were near the detection

limit of the methods used and might produce unrealistically

high ratios. During the first period (March 2004 to March

2005), the microcystin content showed a significant increas-

ing trend (R2 = 0.512, Po 10�4) whereas during the October

2005 to March 2006 period, the microcystin content de-

creased significantly (R2 = 0.569, Po 5.10�3). Planktothrix

agardhii biomass showed opposite trends for these two

periods (see Fig. 2).

Figure 5 shows the relation between P. agardhii mi-

crocystin content and P. agardhii biomass. Planktothrix

agardhii microcystin content tended to be higher when

P. agardhii biomass was lower. Correlation analysis was

unable to detect any significant relationships between the

microcystin content and environmental variables.

(a)

020406080

100120

Mar

-04

Jun-

04

Oct

-04

Jan-

05

Apr

-05

Aug

-05

Nov

-05

Feb

-06

Time

(b)

y = 44.37 Ln x + 130.05

R2 = 0.682

0

20

40

60

80

100

120

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

R2 = 0.388

(c)

10

30

50

0 200 400 600

Fig. 3. (a) The two components of the Planktothrix agardhii biomass

time series: trend (thick line) and residual (�) components. (b) Relation-

ship between P. agardhii (Pa) biomass (in mg L�1) trend values and

orthophosphate (PO43�) concentration (in mM). (c) Relationship between

P. agardhii (Pa) biomass (in mg L�1) residual values and cyclopoid

abundance (in individuals L�1).

FEMS Microbiol Ecol 65 (2008) 61–73 c� 2008 Federation of European Microbiological SocietiesPublished by Blackwell Publishing Ltd. All rights reserved

67P. agardhii bloom collapse and microcystin dynamics

Discussion

Environmental characteristics of the BNV

The limnological characteristics recorded clearly show that

BNV was a hypereutrophic ecosystem according to the mean

and maximum values of standard criteria used for defining

the trophic status of continental surface waters (PO43�, TP,

Chl a, Secchi depth) (Ryding & Rast, 1994). There was a

significant change in the values of some of these indicators

between the two surveys (2001–2002 and 2004–2006).

Moreover, on splitting the last study into 2 consecutive years

(2004–2005 and 2005–2006), the mean values of these

indicators exhibited significant (Po 0.005) changes (e.g.

2� for PO43� and Chl a), all indicating an improvement of

the trophic status of BNV.

These changes can be attributed, at least to some extent,

to the treatment plant that had recently started operation

(Bauer et al., 2003) and induced a reduction of suspended

solids (up to 80%, i.e. 330 tons year�1) originating from the

Table 2. Covariance values between variables

Bivariate

relationships

(paths)

Total

covariancew (A)

Explained covariance

(ridge regression)

Total explained

covariance

(multiple

regression) (D0)

Residual

covariancew

(E = A�D)

Covariance due

to multi-collinearity

(G = D�D0)Directw (B) Indirectz (C) Total (D = B1C)

CYC-Pa � 0.49246��� � 0.35568� – �0.35568 � 0.36034 � 0.13678 0.00466

NAC-Pa � 0.40604�� (� 0.04947) – �0.04947 � 0.04759 � 0.35845 0.00188

CLA-Pa � 0.38179�� (� 0.04563) – �0.04563 � 0.04417 � 0.33616 0.00146

PO43�-Pa 0.50440��� 0.56474��� – 0.56474 0.56854 � 0.06034 �0.00380

NH41-Pa � 0.33280� � 0.30385�� – �0.30385 � 0.30536 � 0.02894 0.00151

W-Pa (0.05640) (0.10988) � 0.03362 0.07537 0.07477 � 0.01898 �0.00060

L-Pa � 0.29572� (� 0.15517) � 0.16780 �0.32068 � 0.32738 � 0.02496 0.00670

T-Pa � 0.32948� (0.00235) � 0.19308 �0.19544 � 0.19815 � 0.13404 0.00271

T-CYC 0.68728��� 0.66029��� – 0.66029 0.67822 0.02699 �0.01793

W-CYC (0.21237) (0.17394) – 0.17394 0.17781 0.03843 �0.00387

T-NAC 0.56323��� 0.56631��� – 0.56631 0.56968 0.00307 �0.00337

W-NAC (� 0.09756) (� 0.12569) – �0.12569 � 0.12660 0.02812 0.00091

T-CLA 0.63880��� 0.62924��� – 0.62924 0.63915 0.00956 �0.00991

W-CLA (0.02580) (� 0.00618) �0.00618 � 0.00678 0.03197 0.00107

T-NH41 (0.12855) (0.12325) – 0.12325 0.12562 0.00530 �0.00237

W-NH41 (0.06389) (0.05650) – 0.05650 0.05749 0.00739 �0.00099

T-PO43� (0.24660) (0.24072) – 0.24072 0.24306 0.00587 �0.00234

W-PO43� (0.08185) (0.06891) – 0.06891 0.06947 0.01294 �0.00056

L-T 0.84683��� 0.84683��� – 0.84683 0.84683 – –

wThe significance of the covariance in columns A and B is:���P � 0.001, ��P � 0.01, �P � 0.05, or nonsignificant (values in parentheses).zThe dashes in columns C and E represent covariance excluded by the model design.

The paths between variables are shown in Fig. 1.

Pa, P. agardhii biomass; CYC, cyclopoid copepods abundance; NAC, copepod nauplii abundance; CLA, cladocerans abundance; T, temperature;

W, average wind speed at 10 m over the 72 h before sampling; L, average global solar radiation between two sampling dates.

0

1

2

3

4

5

6

7

8

Mar

-04

May

-04

Jul-0

4

Sep

-04

Nov

-04

Jan-

05

Mar

-05

May

-05

Jul-0

5

Sep

-05

Nov

-05

Jan-

06

MC

con

cent

ratio

n(µ

g eq

. MC

-LR

L−1

)

Time

0

0.1

0.2

0.3

P.

agar

dhii

MC

con

tent

(µg

eq.

MC

-LR

mg−1

)

∗∗ ∗ ∗Fig. 4. Microcystin concentration (’) in

mg equivalent MC-LR L�1 and Planktothrix agard-

hii microcystin content (gray bars) in mg equiva-

lent MC-LR mg�1 dynamics during years

2004–2006. �, less than detection limit.

FEMS Microbiol Ecol 65 (2008) 61–73c� 2008 Federation of European Microbiological SocietiesPublished by Blackwell Publishing Ltd. All rights reserved

68 A. Catherine et al.

main runoff water collector (75% of the total suspended

solids getting in the BNV). Moreover, during the period

studied, no water from the river Seine, with PO43� concen-

tration higher than the BNV water, entered the lake (B.

Tassin, unpublished data).

Perennial vs. nonperennial P. agardhii dynamics

Planktothrix agardhii biomass values observed during the

years 2001 and 2002 were in the upper part of the range

reported in the literature (e.g. 700 mg L�1 in Lake Albufera;

Romo & Miracle, 1993), while other studies gave lower

maximum values (e.g. 21 mg L�1 in Lough Neagh; Gibson

et al., 2000).

The measurements of the P. agardhii biomass recorded

during the years 2004–2006 in the BNV showed that the

dynamics of this cyanobacterial species was different from

previous years where biomasses were high and stable

(Briand et al., 2002; Yepremian et al., 2007). This study,

using statistical analysis, has shown that the main compo-

nent of P. agardhii dynamics was a significant decreasing

trend (65% of the overall biomass variance). The high

frequency component of the time series did not show any

significant periodicity, at least at the sampling frequency

used in this study.

The fast collapse in P. agardhii biomass, declining from

80 mg L�1 to nearly zero in 3 months, was never observed in

BNV (since 1998, Briand et al., 2002). Such a strong decrease

and even disappearance of P. agardhii after restoration of a

lake or oligotrophication have been reported in other studies

(e.g. Chorus & Wesseler, 1988; Keto & Tallberg, 2000; Kohler

& Hoeg, 2000; Villena & Romo, 2003; Kangro et al., 2005;

Kohler et al., 2005).

Relationship between P. agardhii and pico- andnanophytoplankton dynamics

There was a remarkable negative relation between P. agardhii

biomass and pico- and nanophytoplankton abundance.

According to Mozes et al. (2006), most studies of picophy-

toplankton dynamics in temperate lakes showed a seasonal

trend with highest abundance during the warm water

period. The increase of pico- and nanophytoplankton

abundance in the warm months (from spring to autumn)

of the second year of the survey was not surprising.

The maximum abundance of picophytoplankton

(105 cells mL�1) was in the middle range of the values

reported by Callieri & Stockner (2002) and far from the

highest value reported in hypereutrophic lakes (e.g. Lake

Apopka, Florida; Carrick & Schelske, 1997). These moderate

values of maximum abundance were probably determined

by the low concentration of available inorganic nutrients.

The fact that the pico- and nanophytoplankton abundance

remained low during the warm period of the first year of the

survey is more intriguing and might be linked to the high

P. agardhii biomass observed over the same period. Such a

high biomass may affect (1) the physiology and the growth

of picophytoplankton and/or (2) the trophic links of the

food chain disturbing the grazing processes of protozoa

population such as heterotrophic nanoflagellates and cili-

ates, the most important grazers of small phytoplankton

cells (Callieri & Stockner, 2002).

Factors controlling P. agardhii dynamics

The path analysis model explained almost 66% of P. agardhii

variability, a value comparable to the small number of

results reported in the literature for other potentially toxic

cyanobacteria using multiple regression models (R2 = 0.71

for Microcystis sp. and R2 = 0.47 for Anabaena sp.; Rolland

et al., 2005).

Path-analysis modeling showed that P. agardhii dynamics

was subjected to both bottom-up and top-down control.

PO43� was the variable showing the highest correlation

with the observed changes in P. agardhii biomass. This

variable was also highly significantly related to the P.

agardhii decreasing trend. The best-fit relationship between

PO43� concentrations (x) and P. agardhii biomass trend (y)

was not linear (y = 44.37 Ln x1130.05, Fig. 5), i.e. P. agardhii

biomass did not decrease significantly until PO43� reached

concentrations lower than 0.2mM. This result is in accor-

dance with in vitro and in situ studies showing that the

growth rate of P. agardhii decreased when PO43� concentra-

tions were under 0.3mM (Ahlgren, 1977; Zevenboom et al.,

1982). In BNV, P. agardhii biomass decreased drastically

0

0.05

0.1

0.15

0.2

0.25

0 20 40 60 80 100 120 140 160P. agardhii biomass (mg L−1)

P.

agar

dhii

MC

con

tent

(µg

eq. M

C-L

R m

g−1)

Fig. 5. Relationship between Planktothrix

agardhii microcystin content in mg equiva-

lent MC-LR mg�1 and P. agardhii biomass in

mg�1during the years 2004–2006.

FEMS Microbiol Ecol 65 (2008) 61–73 c� 2008 Federation of European Microbiological SocietiesPublished by Blackwell Publishing Ltd. All rights reserved

69P. agardhii bloom collapse and microcystin dynamics

only when PO43� concentrations were below this threshold

for at least 3 months (August–December 2004), indicating

that the biomass was phosphorus limited. This is consistent

with TP concentration (means, minima and maxima re-

ported in Table 1) changes between the 2001–2002 and

2004–2006 periods. As indicated by these values, in

2001–2002, the carrying capacity of this ecosystem did not

seem to be limited by phosphorus. The system may, for

example, have rather been light limited, whereas in

2004–2006 there will have been a number of occasions for

phosphorus limitation. The increase of L. redekei biomass

after the decrease of P. agardhii (January–May 2005) may

also be linked to the lower phosphorus level as shown by

several authors (Rucker et al., 1997; Wiedner et al., 2002;

Noges & Ott, 2003).

That NH41 concentrations were negatively correlated with

changes in P. agardhii is probably not due to a direct causal

effect. Clearly, the maximum NH41 values observed cannot

be considered to inhibit phytoplankton growth, i.e. to be

toxic (Von Ruckert & Giani, 2004). This negative correlation

could be interpreted as the result of a fast uptake of NH41 by

P. agardhii and/or the result of P. agardhii lysis, leading to

NH41 production by heterotrophic microorganisms through

mineralization of decaying algae. A higher NH41 concentra-

tion associated with a lower PO43� concentration may favor

the growth of small phytoplankton, which can outcompete,

under such circumstances, microphytoplanktonic species

such as P. agardhii. Indeed, it is well known that as a source

of nitrogen, NH41 may favor the growth of small phyto-

plankton species, which are also better adapted to low

nutrient concentration due to their larger surface/volume

ratio (Koike et al., 1986; Harrison & Wood, 1988; Cochlan &

Harrison, 1991).

The negative relation between cyclopoids and P. agardhii

biomass was shown to be associated with the residual

variability component of P. agardhii dynamics. The hypoth-

esis of the negative effects of microcystins on zooplankton

can be ruled out. The microcystin concentrations (max:

7.4mg L�1) measured during this study were far below the

LD50 reported for various zooplankton organisms (e.g. LD50

expressed in mg MC-LR mL�1: Daphnia sp. = 9.6–21.4, De

Mott, 1991; Diaptomus bergei = 0.45–1, De Mott, 1991;

Thamnocephalus platyurus = 0.1, Torokne, 1999). It is more

probable that the negative correlation can be considered as

the result of a top-down control of P. agardhii by cyclopoid

grazing. Although adult CYC are generally considered to be

primarily omnivorous or carnivorous, it has already been

demonstrated that adult females were able to survive and

could reproduce on a diet of algae (Hopp et al., 1997). It has

been found that P. agardhii is an edible prey for various

zooplankton species belonging to cladocerans (Davidowicz

et al., 1988; Gliwicz & Lampert 1990; Degans & De Meester,

2002) and rotifers (Weithoff & Walz, 1995). Even though, it

has been shown that grazing is relatively inefficient when

filamentous cyanobacteria reach large biomass (Davidowicz

et al., 1988), the significant decrease in P. agardhii biomass

may have allowed zooplankton grazing activity to become

more efficient. During the spring and summer of the second

year, the large abundance of zooplankton may have helped

to maintain P. agardhii biomass at a low level.

The control exerted by the zooplankton on P. agardhii

may have been amplified by the decrease in PO43� concentra-

tion, limiting the ability of P. agardhii to compensate the loss

of biomass due to different processes (e.g. grazing).

Among the environmental variables tested as explanatory

variables in the path model, several, especially the tempera-

ture, had no significant correlation with P. agardhii biomass.

This lack of correlation highlighted that Oscillatoriales such as

P. agardhii are able to grow under a wide range of tempera-

tures, even below 10 1C (Post et al., 1985; Noges & Ott 2003).

One can expect to improve such a path-analysis model by

replacing some of the variables used by other ones more

related to biological processes such as grazing, lysis (e.g. viral

lysis) and growth rates. However, these variables are more

difficult to measure in field studies. The results of the path

model can be used to build a predictive equation of P.

agardhii biomass using the path coefficients in Table 2, as

one can do using multiple regression models (Rolland et al.,

2005). Such predictive equations need to be tested on

independent datasets to be validated.

Microcystin concentrations and P. agardhiibiomass dynamics

About 49% of changes in microcystin concentration could

be explained by P. agardhii biomass. This percentage con-

trasts with the nonsignificant or low correlation coefficients

reported by previous studies in the same ecosystem (Briand

et al., 2002; Yepremian et al., 2007) when P. agardhii

biomass was more stable. The collapse in P. agardhii biomass

during the year 2006 is probably the main explanation for

the stronger relationship observed between microcystin

concentration and cyanobacterial biomass. Such a collapse

extended the range of biomass and microcystin concentra-

tion values, giving a better data context to fit a statistical

model. Significant relationships between toxin producer

biomasses and toxin concentrations have also been reported

for studies where a large range of values of these variables

has been recorded (e.g. Rolland et al., 2005; Znachor et al.,

2006; Kardinaal et al., 2007b).

We also showed that the microcystin content per

P. agardhii biomass unit was quite variable and that opposite

trends existed between this ratio and biomass. The same

relationship has been shown recently in several studies of

Microcystis-dominated lakes (Kardinaal & Visser, 2005;

Kardinaal et al., 2007a, b; Welker et al., 2007). Moreover,

FEMS Microbiol Ecol 65 (2008) 61–73c� 2008 Federation of European Microbiological SocietiesPublished by Blackwell Publishing Ltd. All rights reserved

70 A. Catherine et al.

using molecular techniques, these studies reported changes

in the proportion of either microcystin-producing and

-nonproducing genotypes or chemotypes of Microcystis,

which could explain the observed changes in microcystin

concentration. Such P. agardhii microcystin-producing and

-nonproducing subpopulations have already been evidenced

in the BNV Lake (Yepremian et al., 2007) as well as in other

Planktothrix-dominated lakes (Kurmayer et al., 2004; Welker

et al., 2004).

The increase in microcystin amount per unit of P.

agardhii biomass when P. agardhii biomass was low could

be the result of an increase of the proportion of microcystin-

producing subpopulations in response to variations of

environmental parameters. These subpopulations may have

specific ecophysiological properties, allowing their better

persistence than microcystin-nonproducing subpopulations

under adverse conditions. Such differences have been de-

monstrated for Microcystis under light limitation, showing

that microcystin-nonproducing genotypes are stronger

competitors than microcystin-producing ones (Kardinaal

et al., 2007a). Thus, it seems that in dense Microcystis

blooms where shading plays an important role, microcys-

tin-nonproducing genotypes are superior competitors, lead-

ing to seasonal succession. The proportion of these two

genotypes in the 2004–2006 P. agardhii series is being

estimated using molecular methods, and a study of the

ecophysiological properties of several isolated P. agardhii

genotypes is being carried out in the BNV Lake (E. Briand,

unpublished data).

The microcystin cellular content of the microcystin-

producing subpopulations can also be affected by their

physiological status. A positive and significant correlation

between growth and microcystin-production rates has al-

ready been shown for P. agardhii isolated from the BNV

Lake (Yepremian et al., 2007), P. rubescens (Briand et al.,

2005) and Microcystis (Downing et al., 2005).

The improvement of predictive models of microcystin

concentration in lakes, such as the BNV, requires further

investigation of the causes of changes in the microcystin

content of toxin producer biomasses. Depending on the type

of processes involved (physiological status of the cells and/or

subpopulation dynamics), it may be necessary to develop

mechanistic models instead of statistical ones.

Acknowledgements

We are grateful to Aurelie Ledreux for PP2A inhibition

assays. The comments of the two anonymous reviewers are

greatly appreciated. This work was funded by the ECODYN

(INSU no. 04CV131) research program.

References

AFNOR (1982) Eaux, Methodes d’essais: Dosage des

orthophosphates, des polyphosphates et du phosphore total

(methode spectrometrique). AFNOR Ed., Paris.

Ahlgren G (1977) Growth of Oscillatoria agardhii in chemostat

culture 1: nitrogen and phosphorus requirements. Oı kos

29: 209–224.

Bauer T, Prat B & Sauvestre H (2003) Depollution des eaux

pluviales du Collecteur ZOH a Grigny en Essonne. Eau Ind

Nuis 266: 96–101.

Briand J-F, Robillot C, Quiblier-Lloberas C & Bernard C (2002) A

perennial bloom of Planktothrix agardhii (Cyanobacteria) in a

shallow eutrophic French lake: limnological and microcystin

production studies. Arch Hydrobiol 153: 605–622.

Briand J-F, Jacquet S, Flinois C, Avois-Jacquet C, Maisonnette C,

Leberre B & Humbert J-F (2005) Variations in the microcystin

production of Planktothrix rubescens (Cyanobacteria) assessed

from a four-year survey of Lac du Bourget (France) and from

laboratory experiments. Microbiol Ecol 50: 418–428.

Callieri C & Stockner JG (2002) Freshwater autotrophic

picoplankton: a review. J Limnol 61: 1–14.

Campbell L, Nolla HA & Vaulot D (1994) The importance of

Prochlorococcus to community structure in the Central North

Pacific Ocean. Limnol Oceanogr 39: 954–961.

Carrick HJ & Schelske CL (1997) Have we overlooked the

importance of small phytoplankton in productive waters?

Limnol Oceanogr 42: 1613–1621.

Casgrain P & Legendre P (2004) The R package for multivariate

and spatial analysis, Version 4.0 d9 – User’s Manual.

Departement des sciences biologiques, Universite de Montreal,

Canada. http://www.bio.umontreal.ca/Casgrain/en/

Chorus I (2005) Current Approaches to Cyanotoxin Risk

Assessment, Risk Management and Regulations in Different

Countries. Federal Environmental Agency, Dessau.

Chorus I & Wesseler E (1988) Plankton response to lake

restoration in Lake Schlachtensee (Berlin-West). Verh Int

Verein Limnol 23: 719–728.

Cochlan WP & Harrison PJ (1991) Kinetics of nitrogen (nitrate,

ammonium and urea) uptake by the picoflagellate Micromonas

pusilla (Prasinophyceae). J Exp Mar Biol Ecol 153: 129–141.

Codd GA, Bell SG, Kaya K, Ward CJ, Beattie KA & Metcalf JS

(1999) Cyanobacterial toxins, exposure routes and human

health. Eur J Phycol 34: 405–415.

Cottenie K, Michels E, Nuytten N & De Meester L (2003)

Zooplankton metacommunity structure: regional vs. local

processes in highly connected ponds. Ecology 84: 991–1000.

Crosbie ND, Teubner K & Weisse T (2003) Flow-cytometric

mapping provides novel insights into the seasonal and vertical

distributions of freshwater autotrophic picoplankton. Aquat

Microb Ecol 33: 53–66.

Davidowicz P, Gliwicz ZM & Gulati R (1988) Can Daphnia

prevent a blue-green algal bloom in hypertrophic lakes? A

laboratory test. Limnologica 19: 21–26.

Degans H & De Meester L (2002) Top-down control of natural

phyto- and bacterioplankton prey communities by Daphnia

FEMS Microbiol Ecol 65 (2008) 61–73 c� 2008 Federation of European Microbiological SocietiesPublished by Blackwell Publishing Ltd. All rights reserved

71P. agardhii bloom collapse and microcystin dynamics

magna and by the natural zooplankton community of the

eutrophic Lake Blankaart. Hydrobiologia 479: 39–49.

De Mott W (1991) Effects of toxic cyanobacteria and purified

toxins on the survival and feeding of a copepod and three

species of Daphnia. Limnol Oceanogr 36: 1346–1357.

Downing TG, Meyer C, Gehringer MM & Van de Venter M

(2005) Microcystin content of Microcystis aeruginosa is

modulated by nitrogen uptake rate relative to specific growth

rate or carbon fixation rate. Environ Toxicol 20: 257–262.

Gibson CE, Foy RH & Lennox SD (2000) The rise and rise of

Planktothrix agardhii in Lough Neagh 1969–1997. Verh Int

Verein Limnol 27: 2913–2916.

Gliwicz ZM & Lampert W (1990) Food thresholds in Daphnia

species in the absence and presence of blue-green filaments.

Ecology 71: 691–702.

Greenberg A, Clesceri L & Eaton A (1999) Standard Methods for

the Examination of Water and Wastewater, 20th edn. American

Public Health Association, Washington, DC.

Harrison WG & Wood LJE (1988) Inorganic nitrogen uptake by

marine picoplankton: evidence for size partitioning. Limnol

Oceanogr 33: 468–475.

Hocking RR (1976) The analysis and selection of variables in

linear regression. Biometrics 32: 1–49.

Hopp U, Maier G & Bleher R (1997) Reproduction and adult

longevity of five species of planktonic cyclopoid copepods

reared on different diets: a comparative study. Freshwater Biol

38: 289–300.

Kangro K, Laugaste R, Noges P & Ott I (2005) Long-term changes

and seasonal development of phytoplankton in a strongly

stratified hypertrophic lake. Hydrobiologia 547: 91–103.

Kardinaal WEA & Visser PM (2005) Dynamics of cyanobacterial

toxins: sources of variability in microcystin concentrations.

Harmful Cyanobacteria (Huisman J, Matthijs HCP & Visser

PM, eds), pp. 41–63. Springer, Dordrecht.

Kardinaal WEA, Tonk L, Janse I, Hol S, Slot P, Huisman J & Visser

PM (2007a) Competition for light between toxic and non-

toxic strains of the harmful cyanobacterium Microcystis. Appl

Environ Microbiol 73: 2939–2946.

Kardinaal WEA, Janse I, Kamst-van Agterveld M, Meima M,

Snoek J, Mur LR, Huisman J, Zwart G & Visser PM (2007b)

Microcystis genotype succession in relation to microcystin

concentrations in freshwater lakes. Aquat Microb Ecol 48: 1–12.

Keto J & Tallberg P (2000) The recovery of Vesijarvi, a lake in

Southern Finland: water quality and phytoplankton

interpretations. Bor Environ Res 5: 15–26.

Kohler J & Hoeg S (2000) Phytoplankton selection in a river-lake

system during two decades of changing nutrient supply.

Hydrobiologia 424: 13–24.

Kohler J, Hilt S, Adrian R, Nicklisch A, Kozerski HP & Walz N

(2005) Long-term response of a shallow, moderately flushed

lake to reduced external phosphorus and nitrogen loading.

Freshw Biol 50: 1639–1650.

Koike I, Holm-Hansen O & Biggs C (1986) Inorganic nitrogen

metabolism by Antarctic phytoplankton with special reference

to ammonium cycling. Mar Ecol Prog Ser 30: 105–116.

Kurmayer R, Christiansen G, Fastner J & Borner T (2004)

Abundance of active and inactive microcystin genotypes in

populations of the toxic cyanobacterium Planktothrix spp.

Environ Microbiol 6: 831–841.

Legendre L, Frechette M & Legendre P (1981) The contingency

periodogram: a method of identifying rhythms in series of

non-metric ecological data. J Ecol 69: 965–979.

Legendre P & Legendre L (1998) Numerical Ecology, 2nd edn.

Elsevier, Amsterdam.

Levi Y, Harvey M & Cervantes P (2006) Risques sanitaires lies a la

presence de cyanobacteries dans l’eau. Affset Ed., Paris.

Lilliefors HW (1967) The Kolmogorov-Smirnov test for

normality with mean and variance unknown. J Am Stat Ass 62:

399–402.

Lund JWG (1951) A sedimentation technique for counting algae

and other organisms. Hydrobiologia 3: 390–394.

Montealegre RJ, Verreth J, Steenbergen K, Moed J & Machiels M

(1995) A dynamic simulation model for the blooming of

Oscillatoria agardhii in a monomictic lake. Ecol Model 78:

17–24.

Mozes A, Presing M & Voros L (2006) Seasonal dynamics of

picocyanobacteria and picoeukaryotes in a large shallow lake

(Lake Ballaton, Hungary). Int Rev Hydrobiol 91: 38–50.

Noges P & Ott I (2003) Occurrence, coexistence and competition

of Limnothrix redekei and Planktothrix agardhii: analysis of

Danish-Estonian lake database. Arch Hydrobiol Suppl Algol

Stud 109: 429–441.

Post AF, de Wit R & Mur LR (1985) Interactions between

temperature and light intensity on growth and photosynthesis

of the cyanobacterium Oscillatoria agardhii. J Plankton Res 7:

487–495.

Robarts RD & Zohary T (1987) Temperature effects on

photosynthetic capacity, respiration and growth rates of

bloom-forming Cyanobacteria. N Z J Mar Freshw Res 21:

391–399.

Rolland A, Bird DF & Giani A (2005) Seasonal changes in

composition of the cyanobacterial community and the

occurrence of hepatotoxic blooms in the eastern townships,

Quebec, Canada. J Plankt Res 27: 683–694.

Romo S & Miracle M-R (1993) Long-term periodicity of

Plankthothrix agardhii, Pseudanabaena galeata and

Geitlerinema sp. in a shallow hypertrophic lagoon, the

Albufera of Valencia (Spain). Arch Hydrobiol 126: 469–486.

Rucker J, Widener C & Zippel P (1997) Factors controlling the

dominance of Planktothrix agardhii and Limnothrix redekei in

eutrophic shallow lakes. Hydrobiologia 342: 107–115.

Ryding S-O & Rast W (1994) Le controle de l’eutrophisation des

lacs et des reservoirs. Masson Ed, Paris.

Sivonen K (1990) Effects of light, temperature, nitrate,

orthophosphate, and bacteria on growth and hepatotoxin

production by Oscillatoria agardhii strains. App Env Microbiol

56: 2658–2666.

Sokal RR & Rohlf FJ (1981) Biometry: the Principles and Practice

of Statistics in Biological Research, 2nd edn. WH Freeman and

Co., San Francisco, CA.

FEMS Microbiol Ecol 65 (2008) 61–73c� 2008 Federation of European Microbiological SocietiesPublished by Blackwell Publishing Ltd. All rights reserved

72 A. Catherine et al.

Sun J & Liu D (2003) Geometric models for calculating cell

biovolume and area for phytoplankton. J Plankton Res 25:

1331–1346.

Talling JF & Driver D (1963) Some problems in the estimation

of chlorophyll a in phytoplankton. UAEC TID-7633 (ed).

Conference on Primary Production Measurement Marine and

Freshwater, Hawaı, pp. 142–146 .

Torokne AK (1999) A new culture-free microbiotest for routine

detection of cyanobacterial toxins. Environ Toxicol 14:

466–472.

Troussellier M, Courties C & Vaquer A (1993) Recent

applications of flow cytometry in aquatic microbial ecology.

Biol Cell 78: 111–121.

Villena MJ & Romo S (2003) Phytoplankton changes in a shallow

Mediterranean lake (Albufera of Valencia, Spain) after sewage

diversion. Hydrobiologia 506: 281–287.

Von Ruckert G & Giani A (2004) Effect of nitrate and ammonium

on the growth and protein concentration of Microcystis viridis

Lemmermann (Cyanobacteria). Rev Bras Bot 27: 325–331.

Weithoff G & Walz N (1995) Influence of the filamentous

cyanobacterium Planktothrix agardhii on population growth

and reproductive pattern of the rotifer Brachionus calyciflorus.

Hydrobiologia 313: 381–386.

Welker M, Christiansen G & von Dohren H (2004) Diversity of

coexisting Planktothrix (Cyanobacteria) chemotypes deduced

by mass spectral analysis of microcystins and other

oligopeptides. Arch Microbiol 182: 288–298.

Welker M, Sejnohova L, Nemethova D, von Dohren H, Jarkovsky

J & Marsalek B (2007) Seasonal shifts in chemotype

composition of Microcystis sp. communities in the pelagial and

the sediment of a shallow reservoir. Limnol Oceanogr 52:

609–619.

Wiedner C, Nixdorf B, Heinz R, Wirsing B, Neumann U &

Weckesser J (2002) Regulation of cyanobacteria and

microcystin dynamics in polymictic shallow lakes. Arch

Hydrobiol 155: 383–400.

Wiegand C & Pflugmacher S (2005) Ecotoxicological effects of

selected cyanobacterial secondary metabolites: a short review.

Toxicol Appl Pharmacol 203: 201–218.

Willame R, Jurczak T, Iffly J-F, Kull T, Meriluoto J & Hoffmann L

(2005) Distribution of hepatotoxic cyanobacterial blooms in

Belgium and Luxembourg. Hydrobiologia 551: 99–117.

Wright S (1921) Path coefficients and path regressions:

alternative or complementary concepts? Biometrics 16:

189–202.

Yepremian C, Gugger MF, Briand E, Catherine A, Berger C,

Quiblier C & Bernard C (2007) Microcystins ecotypes in a

perennial Planktothrix agardhii bloom. Water Res 41:

4446–4456.

Zevenboom W, Bij de Vaate A & Mur LR (1982) Assessment of

factors limiting growth rate of Oscillatoria agardhii in

hypertrophic Lake Wolderwijd, 1978, by use of physiological

indicators. Limnol Oceanogr 27: 39–52.

Znachor P, Jurczak T, Komarkova J, Jezberova J, Mankiewicz J,

Katovska K & Zapomlova E (2006) Summer changes in

cyanobacterial bloom composition and microcystin

concentration in eutrophic Czech reservoirs. Environ Toxicol

21: 236–243.

FEMS Microbiol Ecol 65 (2008) 61–73 c� 2008 Federation of European Microbiological SocietiesPublished by Blackwell Publishing Ltd. All rights reserved

73P. agardhii bloom collapse and microcystin dynamics