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

Arnaud Catherine, Catherine Quiblier, Claude Yepremian, Patrice Got, Alexis

Groleau, Brigitte Vinçon-Leite, Cecile Bernard, Marc Troussellier

► To cite this version:

Arnaud Catherine, Catherine Quiblier, Claude Yepremian, Patrice Got, Alexis Groleau, et al. | Collapse of a Planktothrix agardhii perennial bloom and microcystin dynamics in response to reduced phosphate concentrations in a temperate lake. FEMS Microbiology Ecology, 2008, 65 (1), pp.61–73. 10.1111/j.1574-6941.2008.00494.x. | hal-00711541

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

Arnaud Catherine¹, Catherine Quiblier¹,², Claude Yéprémian¹, Patrice Got³, Alexis Groleau⁴, Brigitte Vinçon-Leite⁵, Cécile Bernard¹ & Marc Troussellier³

¹USM 505/EA 4105 'Écosystèmes et interactions toxiques', Muséum National d'Histoire Naturelle, Paris, France; ²Université Paris 7, Paris, France; ³UMR-CNRS 5119 'Laboratoire Écosystèmes Lagunaires', Université Montpellier II, Montpellier, France; ⁴UMR-CNRS 7154 IPGP, Université 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 Chaussées, Marne-la-Vallée, France

**Correspondence:** Cécile Bernard, USM 505/EA 4105 Écosystèmes et interactions toxiques, Muséum National d'Histoire Naturelle, 57, rue Cuvier, Case 39, 75231 Paris, Cedex 05, France. Tel.: +33 1 40 79 31 83; fax: +33 1 40 79 35 94; e-mail: cbernard@mnhn.fr

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-Châtillon, 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 $PO_4^{3-}$ 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: < 0.1 µg equivalent MC-L $L^{-1}$; max: 7.4 µg equivalent MC-L $L^{-1}$) 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 physiological 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 (Nõges & 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 eutrophication of these ecosystems. Over the past 10 years, it has been shown that efforts to reduce eutrophication (oligotrophication) of continental aquatic ecosystems could be successful in reducing noxious cyanobacterial populations, such as *P. agardhii* (Keto & Tallberg, 2000; Kangro *et al.*, 2005; Köhler *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
nutrients (e.g. Ahlgren, 1977; Sivonen, 1990; Znachor et al., 2006), temperature (Robarts & Zohary, 1987; Sivonen, 1990), light (Sivonen, 1990) and zooplankton (e.g. Davidowicz 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 variables. The dynamic simulation model of *Oscillatoria agardhii* 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 environmental 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-Châtillon (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 microcystin concentrations (Briand et al., 2002; Yéprémian 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 isolated 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 compared 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-Châtillon' (BNV), a water sports center located (2°23'04.2"E, 48°40'03.3"N) 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 suspended 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 (Météo-France), 6 km far from the study site.

## Physical and chemical variables

### Water temperature, dissolved oxygen and conductivity

Water temperature was measured every 10 min using an *in situ* thermistor chain (four SEAMON-MINI electronic temperature recorders, accuracy 0.05 °C, Hugrun Inc., Reykjavik, 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-µm syringe filter (Nalgene, Rochester, NY). Ammonium ($NH_4^+$) and orthophosphate ($PO_4^{3-}$) 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 µM for $NH_4^+$ and 0.1 µM for $PO_4^{3-}$. The nitrate ($NO_3^-$) concentration was measured using a DX600 ion
chromatograph (Dionex Corp., Westmont, IL) equipped
with an AS14 IonPack analytical column (Dionex Corp.).
The detection limit was 0.85 µM.

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 0.4 µM.

## 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 (> 20 µm) were determined using samples fixed in buffered formaldehyde (5% v/v for high biomass species) or Lugol (for low biomass species) as described previously (Yéprémian *et al.*, 2007). The microphytoplankton units were counted using a Malasez counting chamber with a Nikon Optiphot 2 microscope (× 400, Nikon, Melville) or a Utermöhl 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 < 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 µm) and picophytoplankton (≤ 2 µm) 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 (> 650 nm filter) fluorescence from phycoerythrin and chlorophyll pigments, respectively. For each analysis, fluorescent beads (1, 2, 6, 10, 20 µm, 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 Dickinson) with known concentration.

### Zooplankton communities

Zooplankton was collected by filtering 1.5 L of water through a 60 µm mesh and then preserved in a 70% ethanol solution (Cottenie *et al.*, 2003). Abundances (individuals L⁻¹) were estimated using an inverted microscope (Olympus CK2, Olympus Optical Co.) after sample sedimentation. 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 µg equivalent MC-LR L⁻¹ of raw water.

### Data analyses

The following successive steps were followed: (1) characterization of the components of *P. agardhii* biomass dynamics (time-series analyses), (2) identification of the *P. agardhii* biomass-controlling factors among the different hypothesized ones (path-analysis modeling) and (3) identification of the dynamics components-specific controlling factors (correlation 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. agardhii* biomass and the other measured variables. For instance, both $NH_4^+$ and $NO_3^-$ have first been introduced as potential N sources controlling *P. agardhii* biomasses; only $NH_4^+$ was significantly related to *P. agardhii*, and thus retained in the model. Then, in this study, we chose to use $PO_4^{3-}$ 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 ($r=0.91, P < 10^{-4}$) 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.
<figure><img src="image_2.png" /><figcaption>**Fig. 1.** (a) Path diagram for the proposed general model. Pa, *Planktothrix agardhii* biomass (variable to be explained); T, temperature; L, solar radiation; W, wind speed; NH₄⁺, ammonium concentration; PO₄³⁻, orthophosphate concentration; NAC, copepod nauplii abundance; CYC, cyclopoid copepod abundance; CLA, cladocerans abundance. (b) Significant 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).</figcaption></figure>

The considered bottom-up factors were the concentration of nutrients (NH₄⁺, PO₄³⁻) 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 (<10 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, NH₄⁺, PO₄³⁻ 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 τ matrices. Finally, all variables were normalized (subtracting the mean to each value and dividing 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 procedure (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 (Département des sciences biologiques, Université de Montréal).

## Results

### Limnological characteristics of the BNV

Table 1 presents the mean and range of the main limnological variables recorded during an earlier survey (2001–2002) (Yéprémian *et al.*, 2007) and during this study (2004–2006) of the BNV.

The only variables that were significantly different between the two series were PO₄³⁻ and TP, whose concentrations 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, P < 10⁻⁴, Fig. 2c; TP: R² = 0.692, P < 10⁻⁴).

Vertical temperature profiles did not show any long-term stratification (thermal stratification ≥ 1 °C m⁻¹ did not last more than 4 days over the period studied; data not shown) over the 2004–2006 period.

### Planktothrix agardhii and phytoplankton dynamics

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).
<table><caption>**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</caption><thead><tr><th rowspan="2">Variable</th><th colspan="5">2001–2002</th><th colspan="5">2004–2006</th></tr><tr><th>Mean</th><th>Min</th><th>Max</th><th>SD</th><th>CV (%)</th><th>n</th><th>Mean</th><th>Min</th><th>Max</th><th>SD</th><th>CV (%)</th><th>n</th></tr></thead><tbody><tr><td>T (°C)</td><td>15.2</td><td>4.3</td><td>24.3</td><td>6.9</td><td>45.9</td><td>11</td><td>13.6</td><td>2.7</td><td>26.1</td><td>7.1</td><td>52.1</td><td>52</td></tr><tr><td>DO (mg L<sup>-1</sup>)</td><td>10.8</td><td>9.3</td><td>12.8</td><td>1.3</td><td>12.0</td><td>10</td><td>10.8</td><td>6.6</td><td>14.0</td><td>1.8</td><td>16.7</td><td>36</td></tr><tr><td>DO (%)</td><td>104.4</td><td>88.0</td><td>125.0</td><td>11.2</td><td>10.7</td><td>10</td><td>95.8</td><td>74.4</td><td>133.3</td><td>15.0</td><td>15.7</td><td>29</td></tr><tr><td>C (µS cm<sup>-1</sup>)</td><td>969.9</td><td>933.0</td><td>1011.0</td><td>24.1</td><td>2.5</td><td>10</td><td>938.3</td><td>627.2</td><td>1176.0</td><td>167.5</td><td>17.9</td><td>30</td></tr><tr><td>NH<sub>4</sub><sup>+</sup> (µM)</td><td>1.4</td><td>0.3</td><td>2.7</td><td>0.9</td><td>62.6</td><td>10</td><td>1.8</td><td>0.2</td><td>7.5</td><td>1.7</td><td>94.4</td><td>52</td></tr><tr><td>NO<sub>3</sub><sup>-</sup> (µM)</td><td>8.2</td><td>&lt; DL</td><td>35.9</td><td>10.6</td><td>128.8</td><td>11</td><td>8.6</td><td>&lt; DL</td><td>52.8</td><td>13.5</td><td>156.9</td><td>50</td></tr><tr><td>PO<sub>4</sub><sup>3-</sup> (µM)</td><td>0.6</td><td>0.2</td><td>1.8</td><td>0.5</td><td>74.4</td><td>10</td><td>0.2</td><td>&lt; DL</td><td>0.7</td><td>0.1</td><td>70.2</td><td>52</td></tr><tr><td>TP (µM)</td><td>14.6</td><td>5.6</td><td>27.8</td><td>7.8</td><td>53.2</td><td>10</td><td>3.0</td><td>0.9</td><td>5.8</td><td>1.6</td><td>52.4</td><td>52</td></tr><tr><td>Secchi depth (m)</td><td>0.4</td><td>0.3</td><td>0.4</td><td>0.04</td><td>10.0</td><td>10</td><td>0.6</td><td>0.3</td><td>1.6</td><td>0.3</td><td>45.9</td><td>52</td></tr><tr><td>Chl a (µg L<sup>-1</sup>)</td><td>63.5</td><td>22.0</td><td>136.5</td><td>31.5</td><td>49.6</td><td>18</td><td>63.0</td><td>9.9</td><td>159.6</td><td>32.2</td><td>51.2</td><td>52</td></tr><tr><td>Pa (mg L<sup>-1</sup>)</td><td>166.3</td><td>81.6</td><td>605.2</td><td>157.9</td><td>94.9</td><td>10</td><td>49.3</td><td>0.0</td><td>135.1</td><td>42.8</td><td>86.8</td><td>52</td></tr><tr><td>Pa (%)</td><td>98.0</td><td>92.0</td><td>100.0</td><td>2.9</td><td>2.9</td><td>10</td><td>80.9</td><td>23.2</td><td>100.0</td><td>21.4</td><td>26.4</td><td>52</td></tr><tr><td>MC (µg equ L<sup>-1</sup>)</td><td>9.9</td><td>1.6</td><td>35.0</td><td>8.7</td><td>87.6</td><td>18</td><td>2.2</td><td>&lt; DL</td><td>7.4</td><td>2.0</td><td>93.1</td><td>52</td></tr></tbody></table>

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 Yéprémian *et al.* (2007).

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, unpublished 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 biomass ($R^2$=0.495, $P$ < 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 (> 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 increased. In the first part of this period (from January to March 2005), the only other microphytoplankton species detected in significant amount was the filamentous cyanobacteria *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 represented 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 controlling factors

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 ($X \to Y$, column B) and indirect ($X \to Z \to Y$, 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.
<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Changes in the main biotic and abiotic variables measured during the 2004–2006 study. (a) Chl a concentration in µg L⁻¹ (□), *Planktothrix agardhii* (○) and *Limnothrix redekei* (●) biomasses in mg L⁻¹. (b) Picophytoplankton (▲) and nanophytoplankton (◊) abundances in cells mL⁻¹. (c) NH₄⁺ (♦) and PO₃⁻ (□) concentrations in µM. (d) Cyclopoid abundance (×) in individuals L⁻¹, Temperature (dashed line) in °C.</figcaption></figure>

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 abundance (−0.355, *P* ≤ 0.05) and NH₄⁺ concentration (−0.303, *P* ≤ 0.01). PO₄³⁻ concentration had a positive influence on *P. agardhii* biomass. In contrast, both NH₄⁺ 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.
<figure><img src="image_4.png" /><figcaption>**Fig. 3.** (a) The two components of the *Planktothrix agardhii* biomass time series: trend (thick line) and residual (○) components. (b) Relationship between *P. agardhii* (Pa) biomass (in mg L⁻¹) trend values and orthophosphate (PO₄³⁻) concentration (in µM). (c) Relationship between *P. agardhii* (Pa) biomass (in mg L⁻¹) residual values and cyclopoid abundance (in individuals L⁻¹).</figcaption></figure>

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 explanatory variables (CYC, NH₄⁺, PO₄³⁻). A highly significant relationship (*r*=0.825, *P* ≤ 10⁻³) was observed only between *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, *P* < 10⁻³) (Fig. 3c).

The trend and residual components of the *P. agardhii* dynamics were, therefore, correlated with different controlling variables. The former was positively correlated with PO₄³⁻ concentration and the second was negatively correlated with CYC abundance.

## Microcystin concentrations

Microcystin concentrations estimated during this study ranged between < 0.1 (detection limit) and 7.4 µg equivalent 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 = 0.512+0.034x, R² = 0.49, *P* < 10⁻⁴) explained a significant 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 variations (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 increasing trend (R² = 0.512, *P* < 10⁻⁴) whereas during the October 2005 to March 2006 period, the microcystin content decreased significantly (R² = 0.569, *P* < 5.10⁻³). *Planktothrix agardhii* biomass showed opposite trends for these two periods (see Fig. 2).

Figure 5 shows the relation between *P. agardhii* microcystin 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.
<table><caption>**Table 2.** Covariance values between variables</caption><thead><tr><th rowspan="2">Bivariate<br>relationships<br>(paths)</th><th rowspan="2">Total<br>covariance<sup>†</sup> (A)</th><th colspan="3">Explained covariance<br>(ridge regression)</th><th rowspan="2">Total explained<br>covariance<br>(multiple<br>regression) (D')</th><th rowspan="2">Residual<br>covariance<sup>†</sup><br>(E = A - D)</th><th rowspan="2">Covariance due<br>to multi-collinearity<br>(G = D - D')</th></tr><tr><th>Direct<sup>†</sup> (B)</th><th>Indirect<sup>†</sup> (C)</th><th>Total (D = B+C)</th></tr></thead><tbody><tr><td>CYC-Pa</td><td>-0.49246***</td><td>-0.35568*</td><td>-</td><td>-0.35568</td><td>-0.36034</td><td>-0.13678</td><td>0.00466</td></tr><tr><td>NAC-Pa</td><td>-0.40604**</td><td>(-0.04947)</td><td>-</td><td>-0.04947</td><td>-0.04759</td><td>-0.35845</td><td>0.00188</td></tr><tr><td>CLA-Pa</td><td>-0.38179**</td><td>(-0.04563)</td><td>-</td><td>-0.04563</td><td>-0.04417</td><td>-0.33616</td><td>0.00146</td></tr><tr><td>PO<sub>4</sub><sup>3-</sup>-Pa</td><td>0.50440***</td><td>0.56474***</td><td>-</td><td>0.56474</td><td>0.56854</td><td>-0.06034</td><td>-0.00380</td></tr><tr><td>NH<sub>4</sub><sup>+</sup>-Pa</td><td>-0.33280*</td><td>-0.30385**</td><td>-</td><td>-0.30385</td><td>-0.30536</td><td>-0.02894</td><td>0.00151</td></tr><tr><td>W-Pa</td><td>(0.05640)</td><td>(0.10988)</td><td>-0.03362</td><td>0.07537</td><td>0.07477</td><td>-0.01898</td><td>-0.00060</td></tr><tr><td>L-Pa</td><td>-0.29572*</td><td>(-0.15517)</td><td>-0.16780</td><td>-0.32068</td><td>-0.32738</td><td>-0.02496</td><td>0.00670</td></tr><tr><td>T-Pa</td><td>-0.32948*</td><td>(0.00235)</td><td>-0.19308</td><td>-0.19544</td><td>-0.19815</td><td>-0.13404</td><td>0.00271</td></tr><tr><td>T-CYC</td><td>0.68728***</td><td>0.66029***</td><td>-</td><td>0.66029</td><td>0.67822</td><td>0.02699</td><td>-0.01793</td></tr><tr><td>W-CYC</td><td>(0.21237)</td><td>(0.17394)</td><td>-</td><td>0.17394</td><td>0.17781</td><td>0.03843</td><td>-0.00387</td></tr><tr><td>T-NAC</td><td>0.56323***</td><td>0.56631***</td><td>-</td><td>0.56631</td><td>0.56968</td><td>0.00307</td><td>-0.00337</td></tr><tr><td>W-NAC</td><td>(-0.09756)</td><td>(-0.12569)</td><td>-</td><td>-0.12569</td><td>-0.12660</td><td>0.02812</td><td>0.00091</td></tr><tr><td>T-CLA</td><td>0.63880***</td><td>0.62924***</td><td>-</td><td>0.62924</td><td>0.63915</td><td>0.00956</td><td>-0.00991</td></tr><tr><td>W-CLA</td><td>(0.02580)</td><td>(-0.00618)</td><td>-</td><td>-0.00618</td><td>-0.00678</td><td>0.03197</td><td>0.00107</td></tr><tr><td>T-NH<sub>4</sub><sup>+</sup></td><td>(0.12855)</td><td>(0.12325)</td><td>-</td><td>0.12325</td><td>0.12562</td><td>0.00530</td><td>-0.00237</td></tr><tr><td>W-NH<sub>4</sub><sup>+</sup></td><td>(0.06389)</td><td>(0.05650)</td><td>-</td><td>0.05650</td><td>0.05749</td><td>0.00739</td><td>-0.00099</td></tr><tr><td>T-PO<sub>4</sub><sup>3-</sup></td><td>(0.24660)</td><td>(0.24072)</td><td>-</td><td>0.24072</td><td>0.24306</td><td>0.00587</td><td>-0.00234</td></tr><tr><td>W-PO<sub>4</sub><sup>3-</sup></td><td>(0.08185)</td><td>(0.06891)</td><td>-</td><td>0.06891</td><td>0.06947</td><td>0.01294</td><td>-0.00056</td></tr><tr><td>L-T</td><td>0.84683***</td><td>0.84683***</td><td>-</td><td>0.84683</td><td>0.84683</td><td>-</td><td>-</td></tr></tbody></table>

†The significance of the covariance in columns A and B is:

***P ≤ 0.001, **P ≤ 0.01, *P ≤ 0.05, or nonsignificant (values in parentheses).

‡The 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.

<figure><img src="image_5.png" /><figcaption>**Fig. 4.** Microcystin concentration (■) in µg equivalent MC-LR L⁻¹ and *Planktothrix agardhii* microcystin content (gray bars) in µg equivalent MC-LR mg⁻¹ dynamics during years 2004–2006. *, less than detection limit.</figcaption></figure>

# 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 (*P* < 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
<figure><img src="image_6.png" /><figcaption>**Fig. 5.** Relationship between *Planktothrix agardhii* microcystin content in µg equivalent MC-LR mg⁻¹ and *P. agardhii* biomass in mg⁻¹ during the years 2004–2006.</figcaption></figure>

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⁻ concentration 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; Yéprémian et al., 2007). This study, using statistical analysis, has shown that the main component 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; Köhler & Hoeg, 2000; Villena & Romo, 2003; Kangro et al., 2005; Köhler et al., 2005).

## Relationship between *P. agardhii* and pico- and nanophytoplankton dynamics

There was a remarkable negative relation between *P. agardhii* biomass and pico- and nanophytoplankton abundance. According to Mózes et al. (2006), most studies of picophytoplankton 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 ciliates, 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 x + 130.05, Fig. 5), i.e. *P. agardhii* biomass did not decrease significantly until PO₄³⁻ reached concentrations lower than 0.2 µM. This result is in accordance with *in vitro* and *in situ* studies showing that the growth rate of *P. agardhii* decreased when PO₄³⁻ concentrations were under 0.3 µM (Ahlgren, 1977; Zevenboom et al., 1982). In BNV, *P. agardhii* biomass decreased drastically
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 reported 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 (Rücker *et al.*, 1997; Wiedner *et al.*, 2002; Nöges & Ott, 2003).

That NH₄⁺ concentrations were negatively correlated with changes in *P. agardhii* is probably not due to a direct causal effect. Clearly, the maximum NH₄⁺ values observed cannot be considered to inhibit phytoplankton growth, i.e. to be toxic (Von Rückert & Giani, 2004). This negative correlation could be interpreted as the result of a fast uptake of NH₄⁺ by *P. agardhii* and/or the result of *P. agardhii* lysis, leading to NH₄⁺ production by heterotrophic microorganisms through mineralization of decaying algae. A higher NH₄⁺ concentration 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, NH₄⁺ may favor the growth of small phytoplankton 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 hypothesis of the negative effects of microcystins on zooplankton can be ruled out. The microcystin concentrations (max: 7.4 µg L⁻¹) measured during this study were far below the LD₅₀ reported for various zooplankton organisms (e.g. LD₅₀ expressed in µg 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, Törökne, 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⁻ concentration, 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 temperature, 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 temperatures, even below 10 °C (Post *et al.*, 1985; Nöges & 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. agardhii* biomass dynamics

About 49% of changes in microcystin concentration could be explained by *P. agardhii* biomass. This percentage contrasts with the nonsignificant or low correlation coefficients reported by previous studies in the same ecosystem (Briand *et al.*, 2002; Yéprémian *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 concentration 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,
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 (Yéprémian *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 demonstrated 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, microcystin-nonproducing genotypes are superior competitors, leading 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 (Yéprémian *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 Aurélie 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.

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