# The paradox of re-oligotrophication: the role of bottom-up versus top-down controls on the phytoplankton community

Orlane Anneville, Chun-wei Chang, Gaël Dur, Sami Souissi, Frédéric Rimet, Chih-hao Hsieh

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## To cite this version:

Orlane Anneville, Chun-wei Chang, Gaël Dur, Sami Souissi, Frédéric Rimet, et al.. The paradox of re-oligotrophication: the role of bottom-up versus top-down controls on the phytoplankton community. Oikos, 2019, 128 (11), pp.1666-1677. 10.1111/oik.06399. hal-02544939

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Research

# The paradox of re-oligotrophication: the role of bottom-up versus top-down controls on the phytoplankton community

Orlane Anneville, Chun-Wei Chang, Gaël Dur, Sami Souissi, Frédéric Rimet and Chih-hao Hsieh

O. Anneville (https://orcid.org/0000-0003-1075-5661) ✉ (orlane.anneville@inra.fr), INRA, UMR CARRTEL, 75 bis avenue de Corzent, FR-74200 Thonon les Bains, France. – C.-W. Chang (https://orcid.org/0000-0002-9817-2956), Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan. – G. Dur (https://orcid.org/0000-0003-1703-3047) and F. Rimet (https://orcid.org/0000-0002-5514-869X), INRA, UMR CARRTEL, France. GD also at: Creative Science Unit (Geosciences), Faculty of Science, Shizuoka Univ., Japan. – S. Souissi (https://orcid.org/0000-0002-6720-0096), Univ. de Lille, CNRS, Université du Littoral Côte d'Opale, UMR 8187, LOG, Laboratoire d'Océanologie et de Géosciences, France. – C.-h. Hsieh (https://orcid.org/0000-0001-5935-7272), Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan, and: Inst. of Oceanography, National Taiwan Univ., Taipei, Taiwan.

Oikos

128: 1666–1677, 2019

doi: 10.1111/oik.06399

Subject Editor: Monika Winder

Editor-in-Chief: Dries Bonte

Accepted 23 June 2019

Increases in phytoplankton biomass have been widely observed over the past decades, even in lakes experiencing nutrient reduction. However, the mechanisms giving rise to this trend remain unclear. Here, we unveil the potential mechanisms through quantifying the relative contribution of bottom-up versus top-down control in determining biomasses of phytoplankton assemblages in Lake Geneva. Specifically, we apply nonlinear time series analysis, convergent cross mapping (CCM), to decipher the degree of bottom-up versus top-down control among phytoplankton assemblages via quantifying 1) causal links between environmental factors and various phytoplankton assemblages and 2) the relative importance of bottom-up, top-down, and environmental effects. We show that the recent increase in total phytoplankton biomass, albeit with phosphorus reduction, was mainly caused by a particular phytoplankton assemblage which was better adapted to the re-oligotrophicated environment characterized by relatively low phosphorus concentrations and warm water temperature, and poorly controlled by zooplankton grazing. Our findings suggest that zooplankton act as a critical driver of phytoplankton biomass and strongly impact the dynamics of recovery from eutrophication. Therefore, our phytoplankton assemblage approach in combination with causal identification of top-down versus bottom-up controls provides insights into the reason why phytoplankton biomass may increase in lakes undergoing phosphorus reduction.

Keywords: eutrophication, food-webs, functional groups, global changes, lakes, long-term changes, phytoplankton

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Introduction

During the last century, after periods of severe eutrophication,
many efforts have been made to improve the trophic status
of lakes. For example, efforts to reduce external phospho-
rus loading have often resulted in lower chlorophyll-a con-
centrations, deeper Secchi depths and shifts in community
structure; all indicative of an improvement in water quality
(Jeppesen et al. 2005). Nevertheless, over the last decade, we
have witnessed a trend towards an expansion and prolifera-
tion of undesirable phytoplankton species in some aquatic
habitats (Sukenik et al. 2012, Horn et al. 2015, Salmaso et al.
2015, Morabito et al. 2018, Zohary et al. 2019), even for the
lakes that had undergone re-oligotrophication (Ernst et al.
2009, Michalak et al. 2013).

This raises an important question: why has phytoplank-
ton biomass continued to increase, even in lakes experienc-
ing nutrient reduction? Possible causes for these unexpected
trends may include external or internal nutrient loadings
(Michalak et al. 2013, Ho and Michalak 2017) and climate
change, which affects lake physical conditions and modifies
the outcome of species competition by favoring the spread and
growth of undesirable phytoplankton such as cyanobacteria
(Paerl and Huisman 2009, Carey et al. 2012, Sukenik et al.
2015). However, less emphasis has been put on the effects of
changes in grazing pressure, although the importance of tro-
phic interactions in controlling abundance of phytoplankton
is well recognized (Brett and Goldman 1996, Jeppesen et al.
2000, Carpenter et al. 2001). Thus, assessing the relative
importance of predator regulation versus resource limitation
is a critical step toward a better understanding of temporal
changes in the abundance of phytoplankton (Bunnel et al.
2014). Achieving such a goal can be difficult in aquatic eco-
systems where fluctuating environmental conditions and high
taxonomic diversity result in complex outcomes for resource
utilization, competition and predation. Nevertheless, we
must answer this fundamental ecological question to not
only enhance understanding of ecosystem functioning, but
also inform preventative management.

Here, we demonstrate an effective approach to assess
the importance of top-down and bottom-up control on
phytoplankton by: 1) classification of phytoplankton into
assemblages (Anneville et al. 2002b, 2018a) and 2) applica-
tion of time series methods specifically designed to quantify
causal factors in nonlinear dynamical systems (Sugihara et al.
2012). A functional approach is useful because phytoplank-
ton communities are characterized by a high diversity, with
species varying greatly in size, ecological requirements and
tolerances, such that each taxon may exhibit very different
dynamics (Tilman 1996, Jochimsen et al. 2013). As a result
of this variation in ecological response traits, functional
group composition may change drastically through time,
with no detectable change in total biomass. Thus, analyzing
bulk metrics of phytoplankton abundance (e.g. chlorophyll a
concentrations or total biomass) may mask changes in tem-
poral fluctuations of phytoplankton communities (Vasseur

and Gaedke 2007, Jochimsen et al. 2013, Walters et al. 2013,
Anneville et al. 2018a). As a consequence, distinguishing and
investigating functional group within phytoplankton com-
munities to better explain variability in community abun-
dances is a critical step (Hansson et al. 2004, Kerimoglu et al.
2013). Traditionally, functional groups can be determined
based either on morpho-functional characteristics (Salmaso
and Padisak 2007, Litchman and Klausmeier 2008,
Kruk et al. 2010) or on patterns of spatio-temporal occur-
rence (Margalef 1978, Reynolds et al. 2002). In this study,
we have chosen to adopt the latter framework, since species
that coexist in the same environment should exhibit trait dif-
ferences (Weiher and Keddy 1995, Guislain et al. 2019) but
also share some ecological attributes that increase their fit-
ness under prevailing environmental conditions, leading to
patterns of dominance or co-dominance in a given habitat
(Reynolds et al. 2002). Phytoplankton functional group for
Lake Geneva, used in the present analysis, were identified in
a previous study (Anneville et al. 2018a). Because some of
the groups represent non homogeneous functional traits, we
preferred to use the term ‘assemblage’ instead of ‘functional
group’ in the present analysis.

There are considerable methodological challenges when
seeking to quantify bottom-up and top-down controls in
natural systems. Existing studies using observational data to
analyse causal relationships between variables have largely
relied on linear approaches (e.g. correlation, regression and
structural equation modelling, etc.). However, correlation
does not indicate causation; for instance, spurious correla-
tions between variables can occur as a consequence of shared
determinants (Sugihara et al. 2012). Moreover, a lack of
correlation does not always imply lack-of-causation, either
(Sugihara et al. 2012). In fact, a correlation may appear
to change in magnitude and sign through time (known as
mirage correlation), even though the causal relationship
remains unchanged; this is a hallmark of nonlinear dynami-
cal systems (Deyle et al. 2013, Ye et al. 2015a, b). Mirage
correlations, as well as spurious correlations, create difficul-
ties in the identification of causal effects of bottom-up or
top-down controls, based on linear analyses. To overcome
this issue, we employed a novel approach, convergent cross
mapping (CCM) (Sugihara et al. 2012) that is designed for
analysing nonlinear dynamical systems, to distinguish and
quantify relative strength of bottom-up versus top-down
controls using time series data collected from natural systems
(Matsuzaki et al. 2018).

In this paper CCM was applied to phytoplankton assem-
blages recorded over the past 40 years in Lake Geneva, which
has undergone a severe decrease in phosphorus concentra-
tions but an increase in phytoplankton biomass (Jacquet et al.
2014). The objective of this study was to clarify the role of
bottom-up and top-down regulation of the abundance of
phytoplankton groups in order to better explain the paradox-
ical trend between phytoplankton biomass and phosphorus
concentrations documented in Lake Geneva. Finally, because
unexpected trends in phytoplankton communities have been
observed globally, we use this case study to provide generalizable understanding of the factors underpinning long-term trajectories in phytoplankton communities.

# Material and methods

## Study site

Lake Geneva is a deep and large warm monomictic lake (surface area: 580 km²; volume: 89 km³; maximum depth: 309.7 m; mean depth: 152.7 m), located in the western part of the Alps at an altitude of 372 m. Lake Geneva has undergone important changes in phosphorus concentrations. It has switched from an oligotrophic to eutrophic state (based on total phosphorus OECD criteria, 1982) with annual phosphorus concentrations reaching 90 µg P l⁻¹ in 1979 (Anneville et al. 2002a). Thanks to effective management measures, Lake Geneva was back to a mesotrophic state in the early 2000s with total phosphorus concentrations about 20 µg P l⁻¹ in 2010 (Jacquet et al. 2014). The decrease in phosphorus concentrations coincided with a decrease in the abundance of *Daphnia* sp. (Tadonléké et al. 2009, Anneville et al. 2017) and a spectacular increase in the abundance of zooplanktivorous Coregonids (*Coregonus lavaretus*) (Anneville et al. 2017). In parallel, changes in water temperature were observed as a result of changes in climate cycles (Molinero et al. 2007). Vertical water column structures were not affected in the same way during all seasons (Anneville et al. 2013). Increases in surface water temperature were mainly observed during spring, with a marked shift in 1988 (Woolway et al. 2017) which was induced by change in the North Atlantic Oscillation (Anneville et al. 2002b). Warming of surface waters intensifies vertical stratification and, in accordance with model forecasts (Perroud and Goyette 2010, 2012), Schmidt stability increased significantly (Kraemer et al. 2015) and an earlier onset of thermal stratification was observed (Anneville et al. 2002b, 2013).

## Sampling design

Lake Geneva has been monitored since 1974 as a part of a long-term water quality and biological monitoring program. Sampling has been continuously undertaken in the middle of the lake at the deepest point, once or twice a month. The environmental factors used in this study were selected based on previous analyses emphasizing the role of these factors on phytoplankton abundance in general, or more specifically in Lake Geneva: phosphorus concentration (Frossard et al. 2018), water temperature (Anneville et al. 2002b), abundance of *Daphnia* sp. (Sterner 1989, Lacroix et al. 1996, Pinnel-Alloul et al. 1998, Sommer et al. 2012, Anneville et al. 2017), and incoming solar radiation (Dur et al. unpubl., Deng et al. 2018). Water temperature was measured with a thermometer from 1974 to 1998; a multiprobe was used after 1998. Dissolved phosphorus concentrations (P-PO₄³⁻) were measured at a series of discrete depths between the surface and

the bottom of the lake, using colorimetric analyses and spectrophotometric assay according to a standardized protocol (AFNOR NF EN 1189, Monod et al. 1984).

Water samples for estimating phytoplankton species composition and biomass were collected from the upper 10 m of water column from 1974 to 2001; and after 2001 from the top 18 m, using an integrating bell sampler (Anneville et al. 2002a). The upper 10 m captures the euphotic zone. In the earlier years of the monitoring program, this sampling depth also used to capture the layers of maximum chlorophyll a concentration. However, because the strata in which maximum phytoplankton growth occurs has deepened over time (Fig. 1, Supplementary material Appendix 1), a change in sampling protocol was necessary in order to sample the phytoplankton community representatively (Anneville and Leboulanger 2001). Phytoplankton identifications and cell counts were performed in sedimentation chambers under an inverted microscope (Utermöhl 1958). More than 400 cells were counted per sample. In this study, phytoplankton quantity was expressed in terms of biomass. Species biovolumes were derived from cell numbers and mean cell volumes using geometrical models (Druart and Rimet 2008). Traits and sizes used to compute biovolumes were kept constant for the entire studied period. Complete description of the protocol to calculate biovolumes is provided in Rimet et al. 2018 and morphological traits and sizes of phytoplankton taxa observed in Lake Geneva are available on <https://zenodo.org; DOI: 10.5281/zenodo.1164834>. Finally, species biomass was computed considering a ratio of 1 µg per unit of volume.

*Daphnia* sp. were sampled by a vertical tow from 50 m to the surface using a 200 µm plankton net. The zooplankton samples were preserved in a 5% buffered formaldehyde solution. *Daphnia* sp. were identified and individuals were enumerated in sub-samples that contains at least 0.1 ml zooplankton. *Daphnia* abundances were given as the number of individuals per square meter and were calculated by using the formula:

$$Daphnia \ abundance = N_{\text{SubSample}} \times \frac{V_{\text{Sample}}}{V_{\text{SubSample}}} \times \frac{H_{\text{filtred}}}{V_{\text{filtred}}}$$

where $N_{\text{SubSample}}$ is the number of *Daphnia* in the sub-sample, $V_{\text{Sample}}$ is the volume of the sample, $V_{\text{SubSample}}$ is the volume of the sub-sample, $H_{\text{filtred}}$ is the height of the sampled water column (50 m) and $V_{\text{filtred}}$ is the volume of water filtered by the net (4.81 m³).

## Statistical analyses

### Determining phytoplankton assemblages

The phytoplankton assemblages used in this study were previously identified in Anneville et al. (2018a) using the method proposed in Anneville et al. (2002b, 2004). This method consists of removing rare species, applying a principal component analysis to the log-transformed data, and
<figure><img src="image_6.png" /><figcaption>Figure 1. Temporal changes in environmental conditions and phytoplankton biomass: log-abundance of *Daphnia* sp. (ind. m⁻²), P-PO₄³⁻ concentrations (µg l⁻¹), water temperature (°C), solar radiation (J cm⁻²) and Log-biomass (µg l⁻¹) for total phytoplankton and phytoplankton assemblages (A1, A2, A3, A4 and A5). Grey cells indicate no data.</figcaption></figure>

running a hierarchical classification on the new matrix so that sampling dates could be categorized into various clusters based upon similarities in phytoplankton assemblage structure. Subsequently, the probability of each sampling date belonging to a particular cluster (assemblage type) was computed within a Bayesian framework. For each sampling date, the cluster having the highest membership probability was called the dominant cluster. For each of the dominant clusters, indicator species were selected based on the IndVal index (Dufrêne and Legendre 1997) using 25% threshold IndVal value (Dufrêne and Legendre 1997, Souissi et al. 2001). Such collections of indicator species for each cluster made up phytoplankton assemblages. Five assemblages (A1–A5) were identified (Table 1). They presented strong seasonal succession (Anneville et al. 2018a): A5 exhibited its highest occurrence probability during spring; A3 and A4 were both early summer assemblages with A3 being more dominant since the 90s; for both A1 and A2, the highest occurrence probabilities were in autumn. Nevertheless, A1 was also observed during summer since the mid-1990s. These five assemblages only gathered 34 taxa (out of 400 taxa identified in Lake Geneva), but they contributed to 80% of the sum of the total phytoplankton biomass measured from 1974 to 2010. Temporal changes of the total biomass of those 34 taxa were strongly correlated with the total phytoplankton biomass (r² = 0.97, p-value <0.05).

To assess the contribution of each phytoplankton assemblage to the total community biomass over the period 1974–2010, correlations were used to relate the biomass of each assemblage to the total phytoplankton biomass at the annual

scale. The assemblage with the highest contribution was identified as the main driver of total phytoplankton biomass.

### Environmental factors

We consider the following environmental and biological factors as potential drivers of phytoplankton assemblage structure: solar radiation which significantly contributed to the recent increases in water temperature (Schmid and Köster 2016), average water temperature and dissolved phosphorus concentrations measured in the trophogenic layer (0–20 m), and *Daphnia* sp. abundance. In this study, we focused on *Daphnia* and did not consider other grazers such as copepods. Our rationale is that field observations and experimental studies have shown that *Daphnia* have higher clearance rates than copepods (Sterner 1989). Moreover, in lakes, *Daphnia* are usually identified as the main grazers, and having a strong influence on the dynamics of phytoplankton communities (Sommer et al. 2012). The temporal variability of these factors is characterized by strong annual and inter-annual components (Fig. 1). To characterize environmental conditions associated with each functional phytoplankton group, which also show strong temporal variability (Fig. 1), we used box-and-whiskers plots, to summarize the distribution of environmental parameter values during dates of high and low (above the 3rd quartile and below the 1st quartile, respectively) biomass of each phytoplankton assemblage.

### Convergent cross mapping (CCM)

Empirical dynamic modeling (EDM) was applied to identify causal links between environmental factors
<table><caption>Table 1. List and taxonomic composition of the phytoplankton assemblages (Anneville et al. 2018a) used in the analyses. The table indicates the size categories of the species (M: microphytoplankton, N: nanophytoplankton), the contribution of each assemblage to the total phytoplankton biomass measured over the study period (1974–2010), and the percentage of the variation in total phytoplankton biomass (R-squared) that was explained by the linear model (total phytoplankton biomass regressed against the corresponding phytoplankton assemblage).</caption><thead><tr><th>Phytoplankton assemblages</th><th>Dominant period</th><th>% Total phytoplankton biomass</th><th>R-squared (%)</th></tr></thead><tbody><tr><td>A1<br>Mougeotia gracillima (M), Diatoma tenuis (M), Pseudanabaena limnetica (M), Pseudanabaena galeata (M), Planktothrix rubescens (M)</td><td>autumn and the summer since 1990s</td><td>31.7</td><td>83.5</td></tr><tr><td>A2<br>Closterium aciculare (M), Ulothrix subtilissima (M), Planktothrix rubescens (M), Stephanodiscus binderanus (M), Mallomonas acaroides (M)</td><td>autumn</td><td>6.4</td><td>3.0</td></tr><tr><td>A3<br>Cyclotella costei (N), Dinobryon divergens (M), Ulnaria sp. (M), Dinobryon sociale var. americanum (M), Stichococcus bacillaris (N), Aphanocapsa holsatica (M), Chlamydomonas sp. (N), Synechocystis parvula (N)</td><td>early summer since 1990s</td><td>7.5</td><td>2.8</td></tr><tr><td>A4<br>Ceratium hirundinella (M), Oocystis lacustris (M), Cryptomonas sp. (M), Phacotus lendneri (N), Fragilaria crotonensis (M), Staurastrum cingulum (M), Sphaerocystis schroeteri (M), Cosmarium depressum (M)</td><td>early summer before 1990s</td><td>22.3</td><td>3.9</td></tr><tr><td>A5<br>Stephanodiscus minutulus (N), Chlorella vulgaris (N), Plagioselmis minuta (N), Gymnodinium lantzschii (N), Stephanodiscus alpinus (N), Gymnodinium helveticum (M), Aulacoseira islandica sub sp. Helvetica (M), Asterionella formosa (M), Stephanodiscus neoastraea (M)</td><td>spring</td><td>12.2</td><td>0.1</td></tr></tbody></table>

and the five phytoplankton assemblages over the period 1974–2010, and to assess the relative contribution of top-down versus bottom-up control for each group. Empirical dynamic modeling is nonlinear time series analysis based on Takens embedding theorem (Takens 1981) using state space reconstruction to recover the system dynamics via the lagged embedding of time series data. CCM is a specific case of EDM that is well-suited to distinguishing causality independent of correlation (Chang et al. 2017). CCM infers causation based on information recovery (i.e. effect variables contain encoded information on causal variables), instead of using causal variables to predict effect variables in linear analysis (e.g. Gtanger's causality). Therefore, causations can be identified in CCM when the historical records of effect variables can reliably estimate the states of causal variables inferred from attractor reconstruction (see introductory animation, <http://tinyurl.com/EDM-intro>, Sugihara et al. 2012). That is, if a variable Y is causally influenced by another variable X, then we can use the historical record of Y values to reliably estimate the states of X. This is because X left its 'footprint' in the past history of Y and could be preserved in the time series (Sugihara et al. 2012). In this study, CCM was used to quantify the potential causal environmental drivers for each of the five phytoplankton assemblages. All data were averaged on a monthly basis and time series were de-seasoned (deducting monthly mean and scaled by monthly variance, Ye et al. 2015b). To consider lagged responses, we evaluated lagged CCM (Ye et al. 2015a) up to three months; a relevant time scale for phytoplankton dynamics. Finally, the significance of causation was tested

(see Table in Supplementary material Appendix 1) by testing the convergence of CCM (Sugihara et al. 2012, Chang et al. 2017).

## Data deposition

Data are available from <https://doi.org/10.15454/AOATKG> (Anneville et al. 2019).

## Results

Each of the assemblages used in this study (Table 1) occurred during specific environmental conditions (Fig. 2). The biomass of the spring assemblage (A5) was highest when P-PO₄³⁻ concentrations were high, and both *Daphnia* abundance and water temperature were low. Summer assemblages (A3 and A4) were associated with the highest *Daphnia* sp. abundance recorded in the lake and high global radiation. Similarly to the summer assemblages, A1 and A2 were abundant when P-PO₄³⁻ concentrations were low and the water temperature was high; but they showed high biomass during low solar radiation.

The long-term increase in total phytoplankton biomass occurred at the same time as a strong decrease in P-PO₄³⁻ concentrations and *Daphnia* sp. abundance, as well as an increase in solar radiation and water temperature (Fig. 3). Correlation analysis indicated that only assemblage A1 made a significant contribution to the inter-annual variability of total phytoplankton biomass (Fig. 4). Assemblage A1 included five species and made up, on average, 31.7% of the total
<figure><img src="image_7.png" /><figcaption>Figure 2. Environmental conditions (abundance of *Daphnia* sp., P-PO₄³⁻ concentrations, solar radiation and water temperature) that occur during the sampling dates showing high biomass (above the 3rd quartile, in white boxplots) and low biomass (below the 1st quartile, in grey boxplots) for the different phytoplankton assemblages.</figcaption></figure>

phytoplankton biomass (Table 1). Over the studied period (from 1974 to 2010), A1 explained 83.5% of the variation in total phytoplankton biomass. Its inter-annual dynamics mirrored the dynamics of total phytoplankton biomass (Fig. 3). A1 biomass was low until the mid-1990s; but increased and made a strong contribution to the annual phytoplankton biomass thereafter.

Other groups besides A1 explained only a small amount of variation of total phytoplankton biomass. Assemblage A2 fluctuated but remained at low abundance from 1974 to 2010. Its contribution to the inter-annual variation of total phytoplankton biomass was low and not significant. Assemblages A3 and A4 contained more species than the autumn assemblages and explained respectively 7.5% and 22.3% of the variation in the total phytoplankton biomass. A3 showed a similar trend to A1 but its contribution to the total biomass variability was low (2.8%). A4 was responsible for the high biomass observed in 1975 and 1976 and mirrored the decreasing trend observed in the total phytoplankton biomass from 1975 to 1991. Nevertheless, considering the time period of our analyses, its contribution to the total variability was low (3.9%). Assemblage A5 fluctuated without any clear trend and only explained 0.1% of the variation of total phytoplankton biomass.

Causality networks differed among the phytoplankton assemblages (Fig. 5). The networks indicated that the biomass of phytoplankton can be both top-down and

bottom-up controlled. The spring group A5 was driven by water temperature and *Daphnia* sp. abundance, whereas solar radiation and P-PO₄³⁻ concentration did not appear as significant environmental drivers. Both early summer groups (A3 and A4) were driven by only solar radiation and water temperature. Autumn groups (A1 and A2) were both driven by *Daphnia* sp. abundance, P-PO₄³⁻ concentration, solar radiation and water temperature; furthermore, P-PO₄³⁻ concentration appeared as an important driver in comparison to the other factors, especially for A1. Finally, CCM also revealed strong causal links between P-PO₄³⁻ concentration and *Daphnia* sp. abundance. In addition, *Daphnia* sp. abundance was influenced by the abundance of assemblages A5, A3 and A2, water temperature, and solar radiation. P-PO₄³⁻ concentration was influenced by A5, A4 and A2.

## Discussion

In Lake Geneva, long-term change in the vertical distribution of phytoplankton necessitated the deepening of the sampled-water-layer to ensure representative sampling of the phytoplankton community (Fig. 1, Supplementary material Appendix 1). It is thus mandatory to ensure that change in sampling protocol did not skew results. Accordingly, our results presented no shift in composition or abundance at the date of change in protocol and increase in the variability of A1 biomass occurred just before the change in sampling protocol. Increase in the variability of A1 was caused by three events of high biomass: one in 2001 (4400 µg l⁻¹), 2007 (5836 µg l⁻¹) and 2009 (3215 µg l⁻¹) which are not due to changing sampling protocol that potentially may have caused overestimations. In particular, in 2001, our sampling still followed the 10 m sampling protocol and thus the recorded extreme high biomass cannot be due to a change in the sampling protocol. In 2001, phytoplankton biomass was higher than in 2009. In conclusion, we cannot assume that the high abundance in 2009 was due to a change in sampling protocol. Finally, in 2007, phytoplankton biomasses as well as A1, reached extreme values. Surprisingly, during this year, A1 was abundant not only in summer but throughout the whole year (Anneville et al. 2018a). We suggest this extreme event was caused by a special seasonal dynamic maintaining high abundance throughout the year and thus, unrelated to changing in sampling protocol after 2001.

The concept of bottom-up control strongly underpins many lake management policies supporting recommendation that a reduction in phosphorus loadings will control eutrophication (Smith 1982, Schindler et al. 2008). Accordingly, decreases in phosphorus loading through changes in the use of inorganic fertilizer and improvement of wastewater treatment are expected to cause decreases in lake phosphorus concentrations, albeit with a delay (Jeppesen et al. 2005), and a subsequent decrease in the abundance of phytoplankton. However, recent unexpected increases in phytoplankton biomass, long-side reductions in phosphorus concentrations (i.e. paradox
<figure><img src="image_8.png" /><figcaption>Figure 3. Long-term changes in annual average biomass for total phytoplankton, A1, A2, A3, A4 and A5 phytoplankton assemblages in Lake Geneva as well as environmental variables.</figcaption></figure>

of re-oligotrophication) engendered a need to identify other
causal factors. Among possible explanations, changes in com-
munity structure have attracted attention; in particular, func-
tional strategies allow certain phytoplankton species to adapt
to lower nutrient concentrations, hence maintaining high
overall community biomass (Wentzky et al. 2018). Indeed,
such an explanation stands for Lake Geneva, as demonstrated
in this study (Fig. 4). In addition, we showed that reduced
grazing pressure also plays a role (Fig. 5). This latter factor is
generally poorly investigated in questions related to eutrophi-
cation in large and deep lakes, which are usually thought to
be bottom-up driven (Frossard et al. 2018). Thus, our study
yields important new insights that have an applied relevance
for issues of water quality and ecosystem state.

Our phytoplankton assemblage approach paves the way
to decipher the underlying reasons for such a paradoxical
relationship between phosphorus concentrations and phy-
toplankton trends. Many species coexist in Lake Geneva,
but not all species make an equal contribution to long-term
changes in total phytoplankton biomass. Our results showed
that assemblage A1 alone could explain 83% of the inter-
annual variability in total phytoplankton biomass. Thus,

we argue that if one can interpret the dynamics underlying
this assemblage, it is possible to provide explanations for the
long-term variability of total phytoplankton biomass in Lake
Geneva. Therefore, we centered the following discussion

<figure><img src="image_9.png" /><figcaption>Figure 4. Inter-annual changes in the biomass of each phytoplankton assemblage and their contribution to total phytoplankton biomass.</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>Figure 5. Diagram summarizing the temporal pattern of occurrence for each phytoplankton assemblage and their causal network with environmental factors (PO4: P-PO4³⁻ concentrations, GlobalRad: solar radiation, WaterTemp: water temperature, *Daphnia*: *Daphnia* sp. abundance). Arrows point from the cause to effect; thickness of line indicates the causal strength. Dotted line between Phosphorus and *Daphnia* indicated indirect causations.</figcaption></figure>

on assemblage A1 which contained *Mougeotia gracillima*, *Diatoma tenuis*, *Pseudanabaena limnetica*, *P. galeata* and *Planktothrix rubscens*.

Using CCM, we revealed that the main drivers of A1 biomass include phosphorus concentration, *Daphnia* sp. abundance, water temperature and solar radiation. The abundance of *Daphnia* sp. and phosphorus concentrations were also influenced by other phytoplankton groups. Our results, therefore, depicted a complex network of direct and indirect interactions with both bottom-up and top-down regulation (Fig. 5).

In this study we clearly showed a strong link between the abundance of *Daphnia* sp. and assemblage A1. The abundance of *Daphnia* sp. in Lake Geneva showed an overall decrease between 1986 and 2010 (Laine and Perga 2015, this study) and dropped from 200 000 ind. m⁻² to 64 000 ind. m⁻² (Nóges et al. 2018). After that time period, *Daphnia* continued to decrease at low level of 20 000 ind. m⁻² (Anneville and Laine in press). Possible reasons for the decrease in *Daphnia* abundance are twofold. It may come from a potential decrease in *Daphnia's* food quality as a result of lower phosphorus availability (Sarpe et al. 2014). Or, most probably, it resulted from the increased predation pressure by the whitefish *Coregonus lavaretus* (Nóges et al. 2018). This dominant zooplanktivorous fish in Lake Geneva has increased in density because of the decrease in phosphorus concentrations and warmer waters (Gerdeaux 2004, Anneville et al. 2017). Associated with the decrease in *Daphnia* sp., we noted an increase in assemblage A1 (Fig. 3), suggesting a relaxation of predation pressure on the phytoplankton species composing A1. Our results, therefore, provide strong evidence to support the role of top-down effects in driving phytoplankton abundance in Lake Geneva. This finding, illustrating zooplankton control on phytoplankton

biomass, is not unique and we demonstrated that it strongly impacts the dynamic of recovery from eutrophication. The literature contains several examples where, through trophic cascades, changes in fish and zooplankton communities strongly influence the biomass and composition of phytoplankton (Carpenter et al. 2001, Benndorf et al. 2002, Rollwagen-Bollens et al. 2018). If fishes exert a strong predation pressure on large zooplankters, lower zooplankton abundance in turn may lessen grazing regulation of phytoplankton as shown in our study. Lacustrine fish communities are strongly influenced by changes in trophic status (Gerdeaux et al. 2006, Vonlanthen et al. 2012) and climate (Jeppesen et al. 2012). Because food-web structure moderates the relationship between phosphorus and phytoplankton biomass (Jeppesen et al. 2007, Lepori and Roberts 2017), changes in the fish community can synergistically affect trophic dynamics and regulation mechanisms of phytoplankton communities (Gutierrez et al. 2016). In the current context of global and anthropogenic pressure, lake management strategies should therefore carefully consider variability in fish populations. Finally, top-down controls are generally due to large zooplankton grazers. In this study we did not consider other zooplankton grazers besides *Daphnia*. However, the existence of a top-down control underlined by our results suggests that further research on Lake Geneva zooplankton should investigate the grazing impact of copepods and micro-zooplankton (e.g. rotifers) when data become available. CCM could be used to evaluate the relative contribution of zooplankton taxa in driving changes in the phytoplankton community.

Focusing on assemblage A1, we can still find a significant causal effect of phosphorus concentration on phytoplankton biomass. The question is why a reduction in total phosphorus could elevate the biomass of assemblage A1. Assemblage A1 is the sole group, together with the autumn group A2, affected by phosphorus concentration. The effect of phosphorus is in line with the fact that, in summer and early autumn, the phosphorus concentrations in the euphotic layer are expected to limit phytoplankton growth (Sommer et al. 1986, 2012). The overall decrease in phosphorus loadings in Lake Geneva set environmental conditions under which the species of assemblage A1 became superior competitors, owing to their specific functional traits such as mixotrophy or low-light tolerance (Anneville et al. 2002b). Meanwhile, the inverse relationship between phosphorus and phytoplankton biomass (Fig. 3) suggested that phosphorus poorly controlled the biomass of phytoplankton. In the early stage of lake re-oligotrophication, phytoplankton biomass is frequently insensitive to decreases in phosphorus concentrations (Jeppesen et al. 2005) and can show slow recovery (Marsden 1989, Sas 1989). This phase is usually described as a form of 'hysteresis' (Jorgensen and De Bernardi 1998, Schindler 2012). Hysteresis may result from different processes, such as changes in phytoplankton community composition and functional strategies (Wentzky et al. 2018) or an excess availability of phosphorus in sediments or in deeper water layers,
that results in limitation of phytoplankton growth by other
factors such as nitrogen and grazing (Tilzer et al. 1991). In
Lake Geneva, phosphorus in the euphotic zone potentially
reached the limiting concentration for primary produc-
tion in the late 1980s (Tadonléké et al. 2009). However, it
is now well documented that hydrodynamical processes are
critical for nutrient flux and are effective pathways of nutri-
ent supply for phytoplankton growth (MacIntyre and Jellison
2001, MacIntyre et al. 2006). Accordingly, in Lake Geneva,
nutrient depletion might be punctual because the euphotic
zone is occasionally fertilized by hydrologic events such as
upwelling that bring deeper and rich water to the surface
(Soulignac et al. 2018). Finally, in eutrophic and mesotro-
phic warm monomictic lakes, the euphotic zone is usually
fertilized during winter mixing, and the nutrient availability
in spring may explain why phosphorus concentration do not
control Lake Geneva phytoplankton communities in spring
and early summer.

In contrast to the group-specific effects of top-down
and bottom-up controls, solar radiation and temperature
affected all assemblages (with the exception of A4 that is
not influenced by radiation). Our analyses revealed that
solar radiation contributed to explaining the overall vari-
ability in phytoplankton abundance in Lake Geneva. Such
a finding was confirmed by the observed role of solar radia-
tion on phytoplankton dynamics in other systems (Hu et al.
2009, Deng et al. 2018). In Lake Geneva, the existence of
solar radiation driven long-term cyclicity in phytoplankton
biomass has previsouly been described (Dur et al. unpubl.)

using wavelet coherences and time dependent intrinsic cor-
relation (Huang and Schmitt 2014). Solar radiation gov-
erns a large number of processes, such photosynthesis, and
contributes to the recent warming of a neighbouring lake
in Switzerland (Schmid and Köster 2016). However, more
analyses are now needed to better explain the causal relation-
ships between this atmospheric driver and phytoplankton
dynamics in other systems. Regarding water temperature,
in Lake Geneva, warming influences the onset and inten-
sity of thermal stratification (Anneville et al. 2013) which
are known to initiate phytoplankton growth during spring in
deep lakes (Sommer et al. 1986, Sommer et al. 2012). Also,
the dynamics and intensity of thermal stratification both
strongly influence the development and abundance of some
A1 filamentous species such as *Planktothrix rubescens* and
*Mougeotia gracillima* (Tapolczai et al. 2015). Thus, our results
bring new evidences to support the proposition that increases
in light and water column stability prevent the decrease in
primary production in Lake Geneva (Tadonléké et al. 2009).
Such an increase has promoted earlier spring phytoplankton
growth since the late 1980s in Lake Geneva (Anneville et al.
2018b). Longer spring phytoplankton production leads to
higher phosphorus consumption in spring. When associ-
ated with the decrease in phosphorus loading, it has contrib-
uted to strengthening phosphorus depletion in the euphotic
zone (Anneville et al. 2002b). As a result, due to the indi-
rect effect of temperature, dissolved phosphorus concen-
trations in summer has been reaching low concentrations
and inter-annual changes in vertical profiles of phosphorus

<figure><img src="image_11.png" /><figcaption>Figure 6. Conceptual model of the paradox of the re-oligotrophication: decrease in phosphorus concentration may come with an increase in phytoplankton biomass (Anneville et al. 2018) because 1) decrease in phosphorus concentrations changed fish communities by enhancing the reproduction of whitefish (Anneville et al. 2017), 2) increased whitefish abundance resulted in higher predation on *Daphnia* and 3) decreased *Daphnia* abundance (Nöges et al. 2018), lowered control on phytoplankton (this study).</figcaption></figure>
concentrations have promoted the occurrence of A1 species (Anneville et al 2018a).

## Conclusion

In the context of increased local and global human pressures that can favor growth of certain phytoplankton taxa, identifying the drivers of those phytoplankton using a single lake as a demonstration of underlying mechanisms is critical for establishing efficient water resource management policies. Here we argued, in ecosystems where temporal variability of total phytoplankton is predominantly driven by the variability of a well-identified group, as demonstrated in Lake Geneva, mechanistic understanding of this group is an essential step toward explaining the variability of the overall total biomass.

In this study, we used causal network analysis in combination with a phytoplankton assemblage approach to overcome the challenges involved in assessing the relative role of bottom-up versus top-down controls and identifying the drivers of the critical phytoplankton groups. Our approach successfully revealed the coupled effects of top-down and bottom-up controls that interact and favor the growth of the dominant group responsible for the increase in total phytoplankton biomass. In addition to the finding that decreased phosphorus concentrations have favored development of the low-light adapted species, the drastic decrease in *Daphnia* abundance together with the change in water temperature and solar radiation explain the high phytoplankton biomass observed in recent years (Fig. 6). Ironically, in Lake Geneva, the observed decrease in *Daphnia* abundance most probably resulted from increased predation pressure by a zooplanktivorous fish (Nôges et al. 2018) that benefited from the decrease in phosphorus concentration (Gerdeaux 2004, Anneville et al. 2017). Therefore, with this Lake Geneva case study, we highlight the fact that because of complex interactions within pelagic communities and their environment, the response of aquatic ecosystems to human-induced changes may not follow common intuition. Thus, ecosystem approaches are needed when studying the variability in total phytoplankton biomass, and ideally, management strategies should account for potential impacts of high trophic levels on phytoplankton.

*Acknowledgements* – Authors thank Viet Tran-Khac for his contribution in the drawing of the supplementary figures and Stephen J. Thackeray for English editing.

*Funding* – This is a contribution to the International Associated Laboratory between Lille University and Taiwan. This study is a contribution to the Bio-Asia FASCICLE project that was funded by the French Ministry of Foreign Affairs and the French Ministry of Education in collaboration with the CNRS and INRA for the French partners (www.fascicle.cnrs.fr/). The authors acknowledge the CESAB and John Wesley Powell Center for Analysis and Synthesis for supporting and facilitating the networking through the GEISHA project. The data were from © SOERE OLA-IS, INRA Thonon-les-Bains, CIPEL.

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