1

Effects of re-oligotrophication and climate warming on plankton richness and community stability in a deep mesotrophic lake

Francesco Pomati, Blake Matthews, Jukka Jokela, Andrea Schildknecht and Bas W. Ibelings

F. Pomati (francesco.pomati@eawag.ch) and B. Matthews, Aquatic Ecology, Eawag, Swiss Federal Inst. of Aquatic Science and Technology, CH-6047 Kastanienbaum, Switzerland. – J. Jokela, ETH-Zürich, Inst. of Integrative Biology (IBZ), CH-8092 Zürich, Switzerland and Aquatic Ecology, Eawag, CH-8600 Dübendorf, Switzerland. – A. Schildknecht, Water Utility of Zurich (WVZ), CH-8021 Zurich, Switzerland. – B. W. Ibelings, Netherlands Inst. of Ecology (NIOO-KNAW), NL-6708 PB Wageningen, the Netherlands, and Aquatic Ecology, Eawag, CH-6047 Kastanienbaum, Switzerland.

We studied the effects of re-oligotrophication and climate warming on plankton richness and community stability over a period of 30 years in the deep mesotrophic Lake Zurich (Switzerland). We assembled monthly time-series of phyto-plankton and zooplankton taxonomic richness, phytoplankton functional groups (species with similar functional traits) and physico-chemical environmental descriptors (temperature, conductivity, pH, P-PO4

3, N-NO3, light absorption).

We used multiple linear regression to test: 1) the effect of environmental variability over time and depth on the accrual of plankton richness; and 2) the relative effect of richness and environmental variability on the stability of plankton. Environ-mental change was characterised by increase in temperature, decrease in phosphorus levels, reduced temporal variability of both, and higher heterogeneity of phosphorus over depth (spatial heterogeneity). These conditions occurred concurrently with accrual in plankton taxonomic and functional richness. Increase in temperature and spatial heterogeneity were the best predictors of phytoplankton richness, while phytoplankton richness and spatial heterogeneity had the strongest effects on zooplankton richness. Temporal stability in phytoplankton biovolume was mainly affected by variability in phosphorus and temperature, while zooplankton abundance levels were more strongly linked to fluctuations in nitrogen, temperature and phytoplankton biovolumes. Our analysis highlights that climate warming and re-oligotrophication may favour an increase in spatial (depth) heterogeneity in the water column of deep lakes, enhancing the potential for phytoplankton species co-existence and an increase in plankton richness. Our analysis also suggests that the intensity of fluctuations in key environmental variables can be a better predictor of plankton community stability then average richness.

What determines species richness is one of the most funda-mental questions in ecology and has gained more importance recently due to the negative influence that anthropogenic activity can have on biodiversity at local and global scales (Diaz et al. 2006, Rockström et al. 2009) (www.maweb.org). Studying the processes that generate and maintain diversity in ecosystems is of primary importance in ecology, since biodiversity undoubtedly affects the overall function-ing of an ecosystem (Hooper et al. 2005, Reiss et al. 2009). Climate has been considered as the main driver of species diversity patterns across broad temporal and spatial scales, with spatio-temporal heterogeneity (Shurin et al. 2010, Tittensor et al. 2010, White et al. 2010), stochastic processes and historical contingency being also significant contributors (Hubbell 2001, Shen et al. 2009, Barton et al. 2010, Fukami 2010, Shurin et al. 2010, Tittensor et al. 2010, White et al. 2010). Among human impacts on (aquatic) biodi-versity, eutrophication has caused a number of undesir-able environmental effects worldwide, including a general reduction in species richness (Smith and Schindler 2009) and a loss of ecosystem resilience against further degrada-tion (Folke et al. 2004). Aquatic ecosystems are particularly

rich in biodiversity and at the same time highly sensitive to anthropogenic activity and biodiversity loss (Adrian et al. 2009, Strayer and Dudgeon 2010). In particular, freshwater ecosystems in western countries have experienced decades of eutrophication, which in some cases has been followed by nutrient abatement. Lake restoration programs in many regions started at the peak of the eutrophication period (late 1970s, early 1980s). Resulting reduction in nutrient load-ing coincided with rising temperature from climate warm-ing (Jeppesen et al. 2005, Van Donk et al. 2003). Changes in biodiversity were therefore potentially confounded by simultaneous effects of nutrient reduction and temperature increase (Adrian et al. 2009). Some studies suggest that the effects of warming can mimic the effects of eutrophication in aquatic ecosystems (Paerl and Huisman 2008), but we have limited understanding of how combined climate change and reduction in nutrients affect species richness, ecosystem func-tioning or resilience (Christensen et al. 2006, Ibelings et al. 2007, Reiss et al. 2009). Understanding the relative impor-tance of environmental drivers and species richness on the stability and resilience of aquatic communities may also be crucial to assess effects of anthropogenic impacts on aquatic

Oikos 000: 001–011, 2011 doi: 10.1111/j.1600-0706.2011.20055.x

© 2011 The Authors. Oikos © 2011 Nordic Society Oikos Subject Editor: Lars-Anders Hansson. Accepted 13 September 2011

2

ecosystems (Grman et al. 2010, Shurin et al. 2010). Tackling the above issues requires long-term studies that include both environmental and biodiversity changes, and time series spanning more than a couple of decades are extremely rare in ecology (Adrian et al. 2009, Magurran et al. 2010).

Research on impacts of environmental change on spe-cies richness has examined both spatial and temporal aspects (White et al. 2010). In this article we integrated both approaches by using a dataset from the deep peri-alpine Lake Zurich (Switzerland), covering different lake depths and 31 years (1977–2008) of plankton and environmental data. Our dataset that combines long-term patterns of biodiversity, along with detailed measurements of physical and chemi-cal conditions, is extremely rare and exceedingly valuable to explore how natural communities respond to environmental changes (Magurran et al. 2010). Lake Zurich experienced warming in combination with a decrease in phosphorus concentrations due to local restoration measures applied from the mid 1970s onwards. Surface water temperature of Lake Zurich has increased by 0.24°C per decade, while deep waters have increased by 0.13°C per decade, resulting in a 20% increase of water column stability since 1950s to present times (Livingstone 2003). Total phosphorous has decreased from values approaching 100 mg l1 in the mid 1970s to ca 20 mg l1 at present (Anneville et al. 2005).

Previous studies have investigated the effects of warming and nutrient abatement on physics and limnology of Lake Zurich, including changes in the composition of phyto-plankton communities and their ecological characteristics (Livingstone 2003, Anneville et al. 2004, 2005). Warmer water temperatures in Lake Zurich affected winter phy-toplankton community composition, promoting the dominance of the filamentous and toxic cyanobacterium Planktothrix rubescens, while oligotrophication favoured species adapted to low nutrient and low light intensities (Anneville et al. 2004). We focused the present article on changes in phytoplankton and zooplankton community richness. With the aim of comparing patterns in functional richness to taxonomic richness, we applied a classification of phytoplankton into groups based on similar functional traits, i.e. morphological, physiological or phenological characters affecting species performance (Padisák et al. 2009, Reynolds et al. 2002). It has been argued that patterns of functional diversity may afford better insight into processes of ecosys-tem change and responses (Suding et al. 2008, Hillebrand and Matthiessen 2009, Reiss et al. 2009).

We had the two following goals for this study. First, we investigated how multiple long-term drivers of environmen-tal change, namely temperature, pH, conductivity, dissolved phosphorus, nitrogen and light attenuation, affected the trend in plankton species and functional richness over three decades. Second, we tested the relative importance of average plankton richness and temporal variability in the same set of environmental drivers on the month-to-month stability of phytoplankton biovolumes and zooplankton abundance. Specifically, we report: 1) trends in environmental physical conditions, available resources and their spatial (depth) het-erogeneity; 2) patterns in phytoplankton and zooplankton taxonomic richness, and phytoplankton functional group richness; 3) temporal variability in environmental physical conditions and available resources; 4) trends in plankton

biovolumes and its variability; 5) potential drivers of plank-ton richness and plankton community stability.

Methods

Lake data

Lake Zurich is located at 406 m above sea level on the Swiss Plateau, immediately to the north of the Swiss Alps. With a maximum depth of 136 m, a surface area of 65 km2 and a volume of 3.3 km3, it is one of the largest European peri- alpine lakes and serves as a source of drinking water for almost 1 million people. The complete overturn of the lake during winter mixing does not occur every year and the fre-quency with which complete overturn occurs is decreasing as a consequence of climate warming (Peeters et al. 2002, Anneville et al. 2004).

Lake Zurich has been monitored for decades with a monthly frequency as part of the monitoring program by the Zurich Water Supply Company (WVZ). Sampling of physi-cal and biological variables occurred at the deepest point of the lake; depth resolution is comparatively fine for a long term historical lake data-set, and includes both physical and biological parameters at: 0, 1, 2.5, 5, 7.5, 10, 12.5, 15, 20, 30, 40, 80, 120 and 135 m. We used data from 1977 to 2008, which included chemistry, physics and phytoplank-ton for each of the above described 14 depths. Zooplankton counts, mainly targeting crustaceans (no rotifer and ciliates), were obtained by plankton net-collection (95 mm pore size) from bottom (136 m) to top (0 m) in the same location and at same date of the other parameters. Phytoplankton identi-fication, counts and measurements were carried out under an inverted microscope after sedimentation in chambers. More details on phytoplankton counts and biovolumes or analyti-cal methods utilized for physical-chemical parameters can be found elsewhere (Livingstone 2003, Peeters et al. 2002, Anneville et al. 2005).

Counting of phyto- and zooplankton has been performed in a highly consistent manner over the years. A general prob-lem with long term data-sets is the possibility that species richness has been biased by human factors such as training or turnover of personnel. We have good confidence with the quality of presented data since they have been acquired by only a very small team of specialists under the supervised of the same person during the entire period. For this study, we re-assigned all plankton species to the modern taxo-nomic affiliations with taxon names matched to the ITIS Catalogue of Life Hierarchy (http://data.gbif.org/datasets/resource/1542). We classified each phytoplankton species to a functional association following the system proposed by Reynolds and others (Reynolds et al. 2002, Padisák et al. 2009). Reynolds categories are functional associations of species selected on the basis of their ecological and adap-tive features: species are grouped based on similar morpho-logical and physiological traits, and on similar ecological requirements (such as high affinity for phosphorus or CO2, requirement of skeletal silicon, motility and mixotrophy). Systems for a functional classification of zooplankton have been proposed (Barnett et al. 2007), but were not applicable to the Lake Zurich dataset.

3

Some methodologies have changed over the period cov-ered by this study rendering the measurement of certain vari-ables incomparable over time. For the analysis reported here we chose six physical-chemical variables. Criteria for selection included 1) consistency of measures at all sampling depths on every date of the time-series using the same method, 2) relevance as habitat physical condition or role as specific plankton nutrient and 3) low correlation with other chosen variables. Our selection includes temperature, conductivity and pH as environmental physical conditions and P-PO4

3, N-NO3

and UV light attenuation (as measured at 254 nm, A254 as plankton resources. The latter parameter (A254), origi-nally used as a measure of DOC, shows a high correlation to PAR light absorption. Regression analysis of a random selection of dozens of lakes from the US EPA Eastern Lake Survey in which absorption coefficients are measured at dif-ferent wavelengths (Bukaveckas and Robbins-Forbes 2000) shows that light absorption in PAR and UV regions of the spectrum is highly correlated (R2

adj 0.935, p 0.0001). As a result, it is justified to use A254 as an index of light atten-uation in the water column of Lake Zurich.

Statistical analyses

All time-series covered all sampled depths and had a monthly frequency between 1977 and 2008 (n 384). Phytoplank-ton depth data were collapsed to average water column val-ues in order to allow comparison with zooplankton net data. Depth variability in physical-chemical parameters was cal-culated as the standard deviation (SD) divided by the aver-age of raw data (14 depths) for each day of the time series (coefficient of variation - CV). The intensity of temporal fluctuations in biological and physical-chemical parameters (e.g. time variability) was obtained by estimating the SD of data within a five year moving window (n 60) sliding over the residuals of de-seasonalised and de-trended time-series of average water-column values. In our study, we used depth variability and time variability as measures of spatial (depth) heterogeneity and temporal stability, respectively.

Data analysis and graphics were performed in the R statistical programming language (www.r-project.org). Time series of biological, physical and chemical variables were decomposed in long-term trend, seasonal trend and residual variability by locally weighted scatterplot smoothing (loess) using the stl function implemented in the R package stats (Cleveland et al. 1990). We performed principal com-ponent analysis (PCA) to evaluate the time-trajectory of Lake Zurich in a multi-dimensional space defined by general physical conditions and specific plankton resources. PCA was performed on the multivariate and scaled environmental data matrix comprising de-seasonalised trends (to exclude seasonal variation) in average water-column values of temperature, conductivity, pH, P-PO4

3, N-NO3 and light absorption.

Time was not included as a variable in the PCA.The significance of slopes in the time-series was estimated

by regressing raw data versus time using generalised linear mixed models. In this study we aimed at assessing the rela-tive importance of several environmental forcing factors in trends of plankton richness and community stability. We therefore set aside autocorrelation, which can explain a sig-nificant proportion of the variance in our time-series (data

not shown), and modelled the relationship between environ-mental drivers and plankton response variables (Legendre and Legendre 1998). We used multiple linear regression models to assess the effects of: 1) ecosystem variables on plankton richness and 2) plankton richness and environ-mental time-variability on the stability of phytoplankton biovolume and zoo plankton density. For regression analy-sis (1) we used de-seasonalised trends (n 384) (Legendre and Legendre 1998). For analysis (2), the time variability of environmental physical conditions, calculated as reported above on de-seasonalised residuals (Supplementary mate-rial Appendix 2), was matched by average plankton richness similarly calculated within a sliding five-year long tempo-ral window applied to raw data (n 324 for all variables) (Supplementary material Appendix 4). In both multiple linear regression analyses, response variables were scaled in order to standardise effect sizes.

We evaluated the collinearity in the explanatory variables of our regression models by calculating variance inflation factors (VIFs) (O’Brien 2007): all variables showed VIFs 10, apart from phytoplankton richness in the zooplankton richness model with a VIF 19. We considered these lev-els of multi-collinearity as acceptable for the purpose of our analysis, and the case of phytoplankton richness in the zoo-plankton richness model will be specifically addressed (see Interacting spatio-temporal drivers of plankton trends in Discussion). Our models were robust with regards to multi-collinearity effects as assessed by principal component regres-sion and stepwise regression (alternation of forward selection and backward elimination of variables based on p 0.05) (Legendre and Legendre 1998). Bootstrapping confidence intervals confirmed the relative importance of regressors (their R2 contribution) (data not shown).

Results

Environmental change

We used PCA to describe the trajectory of Lake Zurich envi-ronmental change in a space defined by long-term trends in temperature, pH, conductivity, P-PO4

3, N-NO3, and light

absorption (A254). This list includes indicators of change in environmental physical conditions and plankton resources. Early in the time-series (late 1970s) the lake was character-ised by higher levels of dissolved phosphorus (P-PO4

3), lower temperature and higher levels of light penetration (Fig. 1). The lake moved to a state with higher levels of dissolved nitrogen (N-NO3

) in the middle of the time- series (1980s), finally shifting to a recent condition of lower nutrient-concentrations, higher temperature, elevated pH and conductivity. The first axis of environmental change in Lake Zurich appeared to be more closely related to indica-tors of general physical-chemical conditions of the water, such as temperature and conductivity (Table 1). The second axis of environmental change was associated with dynamics in plankton resource levels, in particular P-PO4

3 and light absorption (Table 1).

We analysed trends in environmental lake physical con-ditions and phytoplankton resources (Fig. 2, Supplemen-tary material Appendix 1) and assessed the significance of

4

Figure 1. Principal component analysis of Lake Zurich environ-mental variables utilised in this study (see Table 1 for PCA factor loadings). The plot represents the time-trajectory of PC 1 vs PC 2.

Table 1. Factor loadings of PCA using selected environmental vari-ables of Lake Zurich. Results refer to PCA performed with de-trended time-series.

PC 1 PC 2 PC 3 PC 4 PC 5 PC 6

SD 1.496 1.299 1.009 0.697 0.613 0.442Variance 0.373 0.281 0.17 0.081 0.063 0.032Cumulative 0.373 0.654 0.824 0.905 0.967 1Temperature 0.473 0.316 0.472 0.165 0.191 0.624Conductivity 0.538 0.131 0.320 0.261 0.682 0.239pH 0.413 0.221 0.599 0.457 0.387 0.250P-PO4

3 0.309 0.619 0.076 0.296 0.194 0.625N-NO3

0.466 0.312 0.315 0.537 0.530 0.130Light 0.057 0.594 0.458 0.566 0.171 0.291

linear trend slopes over time (Table 2). Temperature and conductivity showed a statistically significant increase over time, while resources like P-PO4

3 and light absorption sig-nificantly decreased (Table 2). We focused on the two most important climatic and plankton resource drivers in Lake Zurich, temperature and P-PO4

3, and on their variability over time and by depth. The time variability in temperature de-seasonalised residuals showed a tendency towards a slight reduction (Fig. 2), which was more evident after excluding from the trend the peak at the end of the time-series, due to an exceptionally warm winter in 2006 (data not shown). We observed a strong decrease in the intensity of temporal fluctuations of P-PO4

3 de-seasonalised residuals (Fig. 2). A decrease in the intensity of temporal fluctuations was also evident in the de-seasonalised residuals of conductivity and pH, while time-series of N-NO3

and light absorp-tion appeared to signal a period of high temporal variabil-ity between 1999 and 2003, and between 2000 and 2005, respectively (Supplementary material Appendix 2).

We explored the variability by depth (expressed as CV) for environmental physical conditions and plankton lim-iting resources as a mean to assess changes in the scale of vertical (depth) heterogeneity of the water column (Fig. 2,

Supplementary material Appendix 3). Phosphorus variabil-ity by depth showed the most significant increase over time (Fig. 2, Table 2), suggesting a strong temporal gradient in the spatial heterogeneity of this essential phytoplankton nutrient. In our analysis, the depth variability of most other variables, including temperature, did not significantly increase over time (Fig. 2, Table 2, Supplementary material Appendix 2). Previous work on Lake Zurich data, however, demonstrated a significant temporal increment (10%) in the thermal stability of the water column between mid 1970s and 2000 (Livingstone 2003). These results combined with previous evidence indicate that Lake Zurich environ-ment has become more oligotrophic, more temporally stable and more heterogeneous over the vertical (spatial) dimension of the water column over the period of study.

Dynamics of plankton richness

Between 1977 and 2008, Lake Zurich was characterised by a strong accrual in species richness for both phyto- and zoo-plankton (Fig. 3). For phytoplankton, the increase in rich-ness was apparent at all taxonomic levels (up to Phyla, data not shown) including species and families (Fig. 3), and for functional associations of species (Reynolds groups, Fig. 3). Richness in phytoplankton species and families increased from ca 40 to 100 and ca 25 to 45 units, respectively. The increase in taxonomic richness over time fell within the same order of magnitude for phytoplankton species, families, and zooplankton species in this order of slope steepness (Table 2). The slope in accrual of Reynolds groups was one order of magnitude lower than phytoplankton species (Table 2).

Drivers of plankton richness

To assess the relative importance of environmental forcing on the accrual of plankton richness we regressed de-seasonalised trends in lake physical conditions and available resources, and their variability by depth on plankton richness trends (Table 3). We also included the richness of zooplankton and phytoplankton species as explanatory variables for phyto-plankton and zooplankton trends, respectively. The variables that were more strongly and positively related to the trend in phytoplankton species richness were: variability by depth in P-PO4

3 and temperature, number of zooplankton species and water temperature, in this order (Table 3). The trend in Reynolds groups was more strongly and positively correlated to the variability by depth in temperature, conductivity and P-PO4

3 (Table 3). The strongest correlate to the trend in zooplankton species richness was phytoplankton species rich-ness with a positive effect, followed by the negative effects of water temperature and variability by depth in temperature and conductivity (Table 3). Overall, our results suggested that the variability by depth (heterogeneity) in environmen-tal physical conditions may represent the strongest driver of increasing trends in plankton richness.

Dynamics in plankton density

The increase in plankton species richness coincided with a statistically significant increase in phytoplankton total bio-volume and zooplankton total density (Fig. 4, Table 2).

5

Figure 2. Time-series of Lake Zurich water temperature and P-PO43 (depth-averaged levels). Each variable is joined by an analysis of the

fluctuations (SD) within a five-year window sliding over de-seasonalised residuals of the time-series. Horizontal dashed arrows show the width of the moving window used to compute SD (Material and methods). The SD relative to average values (CV) calculated between different depths over the time-series is also presented as an indication of changes in vertical (depth) heterogeneity in temperature and P-PO4

3. Black line raw data; dark grey line trend. For significance of linear fit slopes see Table 2.

Analysis of de-seasonalised residuals in the above time-series highlighted a decrease in the intensity of temporal fluctua-tions of phytoplankton total biovolume over the period of study, as indicated by a decrease in the SD calculated within a window of five years sliding over the time-series (Fig. 4). In zooplankton, density fluctuations showed periods of higher variability followed by short periods of relative stability (Fig. 4).

Drivers of plankton community stability

We aimed at determining the relative importance of fluc-tuation intensity in lake physical conditions and available resources, as well as average plankton richness, in explain-ing the temporal variability of phytoplankton and zoo-plankton communities. We regressed average plankton richness (calculated within a window of five years sliding over raw time-series, Supplementary material Appendix 4) and time variability in lake parameters (Supplementary mate-rial Appendix 2) on time variability in phytoplankton total biovolume and zooplankton total density, respectively (Fig. 4). The intensity of temporal fluctuation in phytoplankton

biovolume was more strongly and positively related to the temporal variability in P-PO4

3 and temperature, in this order of importance (Table 4). Temporal variability in phy-toplankton total biovolume seemed to be negatively corre-lated to average levels of phytoplankton functional richness (Reynolds groups) and zooplankton species richness, to fluctuations in zooplankton density but not to higher phy-toplankton species richness (Table 4). Temporal variability in zooplankton total density was significantly and negatively correlated to the intensity of fluctuations in N-NO3

, phy-toplankton biovolumes and pH, and positively related to time variability in water temperature (Table 4). Our results suggest that, in our data, stability in the dynamics of phy-toplankton biovolume and zooplankton density was mostly associated with changes in environmental variability.

Discussion

We observed spatial and temporal changes in the environ-ment occurring together with a strong increase in plankton richness at the taxonomic, functional and trophic levels over

6

Figure 3. Time-series of phytoplankton richness in species and families, number of Reynolds groups and zooplankton species in Lake Zurich. Black line raw data; dark grey line trend. Slopes and intercepts of linear trends are reported in Table 2.

Table 2. Linear fit slopes of generalised mixed models on the relationship between the investigated ecosystem variables (depth-average) and time. Significant slopes are highlighted in bold. *All intercepts are highly significant (p 0.001).

Slopes p-value Intercepts*

Temperature 1.09 1004 0.0010 6.900Conductivity 4.76 1004 0.0001 248.431pH 3.30 1006 0.0610 7.868P-PO4

3 5.00 1003 0.0000 71.305N-NO3

2.00 1003 0.0720 706.959Light abs (254 nm) 1.57 1005 0.0004 2.880CV-depth Temperature 3.15 1006 0.4010 0.380CV-depth Conductivity 6.13 1007 0.3040 0.048CV-depth pH 4.05 1007 0.0530 0.035CV-depth P-PO4

3 5.69 1005 0.0000 0.707CV-depth N-NO3

9.25 1008 0.9640 0.192CV-depth Light abs 9.48 1007 0.0660 0.045Phytoplankton species

num.5.00 1003 0.0000 31.834

Phytoplankton family num.

2.00 1003 0.0000 25.730

Reynolds groups num. 5.00 1004 0.0000 17.712Zooplankton species

num.1.00 1003 0.0000 6.977

Log(mg l1) Phyto-plankton

2.24 1005 0.0020 6.767

Log(counts m2) Zooplankton

5.82 1005 0.0000 13.183

a period of 31 years in a temperate, deep lake. The environ-ment favouring plankton diversity appeared to be temporally more stable, more oligotrophic and more spatially (depth) heterogeneous. We also found that climate warming and re-oligotrophication in Lake Zurich were associated with higher plankton density, as well as by a decrease in the intensity of temporal fluctuations in environmental physical conditions, P-PO4

3 and plankton abundance. Below we discuss the observed spatio-temporal patterns and possible mechanisms driving accrual in plankton richness.

Patterns in plankton richness and abundance

Patterns of increase in species richness and rates of tempo-ral turn-over can vary among ecosystem types, depending on local environmental factors, but also among taxa and trophic levels (Korhonen et al. 2010, Magurran et al. 2010). Our data show comparable slopes for the increase in phyto- and zooplankton species over time, suggesting that species richness accrual in these two trophic levels could be tightly linked (see Interacting spatio-temporal drivers of plankton trends). Our results also highlight that accrual in functional associations of phytoplankton, such as Reynolds groups, was slow compared to taxonomic richness (Fig. 3, Table 2). We observed Reynolds groups reaching a maximum of 25 units in periods characterised by more than 100 phytoplankton species (Fig. 3). Currently, there are 40 categories in the clas-sification method initially proposed by Reynolds to group different phytoplankton species into functional associations (Reynolds et al. 2002, Padisák et al. 2009).

Reynolds groups include species that share similar morpho-physiological traits, evolutionary strategies or eco-logical requirements that are often taxonomically unrelated

(Reynolds et al. 2002, Padisák et al. 2009). Taxonomic clas-sification of phytoplankton in fact does not necessarily reflect the species’ ecological function (Reynolds et al. 2002) and measures of functional diversity may afford a better descrip-tion of the functionality of the ecosystem and its resilience (Hillebrand and Matthiessen 2009, Reiss et al. 2009). Other studies have shown that taxonomic and functional diversity are positively but weakly related (Longhi and Beisner 2010). In our study, the slower accrual in phytoplankton functional associations compared to that of species may be the result of the limited number of Reynolds categories compared to species, and of redundancy in ecological functions among taxa (several species that appear over time are assigned to the same functional association).

The accrual in phytoplankton species richness and func-tional associations may explain the increase in biomass per unit of available nutrients over time, which in turn may have affected total zooplankton abundance (Fig. 3–4). When spe-cies use resources in complementary ways, diverse commu-nities should exploit the available resources (nutrients and light) more fully and produce higher biomass levels than

7

Table 3. Relative effects of ecosystem variables (depth-average) on plankton richness assessed by multiple linear regression on de-seasonalised trends. Variables were scaled to standardise effect sizes. The strongest and most significant effects are highlighted in bold.

Phytoplankton Zooplankton

Species richness Reynolds group richness Species richness

Effects p-values Effects p-values Effects p-values

(Intercept) 0.00001 0.99885 0.00095 0.91093 0.00192 0.84961Temperature 0.42515 0.00000 0.08726 0.24157 0.33256 0.00032Conductivity 0.10140 0.00001 0.08351 0.00053 0.03750 0.20121pH 0.00536 0.83117 0.01049 0.68786 0.00619 0.84315P-PO4

3 0.09648 0.00098 0.21792 0.00000 0.27704 0.00000N-NO3

0.22442 0.00000 0.19093 0.00000 0.14882 0.00246Light abs (254 nm) 0.13412 0.00000 0.09441 0.00000 0.09905 0.00000CV-depth Temperature 0.51805 0.00000 0.64981 0.00000 0.49277 0.00041CV-depth Conductivity 0.21976 0.00306 0.54398 0.00000 0.42520 0.00000CV-depth pH 0.28695 0.00000 0.23205 0.00001 0.11290 0.07794CV-depth P-PO4

3 0.67312 0.00000 0.51434 0.00000 0.31506 0.00000CV-depth N-NO3

0.13217 0.04702 0.22093 0.00146 0.04552 0.58353CV-depth Light abs 0.36191 0.00000 0.37564 0.00000 0.19334 0.00017Zooplankton richness 0.50860 0.00000 0.32275 0.00000 \ \Phytoplankton richness \ \ \ \ 0.78793 0.00000

Figure 4. Trends in phytoplankton total biovolume and zooplankton density (individuals for square meter of water surface) and their tem-poral variability (SD) within a five-year window sliding over de-seasonalised residuals of the time-series. Horizontal dashed arrows show the width of the moving window used to compute SD. Black line raw data; dark grey line trend. For linear fit slopes and significance see Table 2.

8

gradients and enhanced nutrient limitation of phytoplank-ton growth. In this study we found that the heterogeneity of P-PO4

3 between different depths markedly increased along the time series (Fig. 2), which could be due to the water column becoming both structurally more stable and more oligotrophic.

Our results showed that both the increase in temperature and environmental heterogeneity by depth are significant factors affecting the temporal accrual in plankton richness (Table 3). Water temperature alone has been found to be the primary environmental correlate of aquatic diversity, includ-ing plankton, over large geographic scales in marine and freshwater environments (Tittensor et al. 2010, Stomp et al. 2011). Our results suggest a negative relationship between zooplankton species richness and long-term average temper-ature (Table 3), similar to data published in a meta-analysis of plankton diversity across lakes (Shurin et al. 2010). Our data were however biased by the strong cross-correlation of temperature with phytoplankton richness. Zooplankton richness appears to be mainly explained by the phytoplank-ton species number, taken as single explanatory variable or in combination with others (data not shown). If phytoplankton richness is excluded from the model in Table 3, temperature appears to have a strong positive effect on zooplankton spe-cies accrual. Zooplankton richness appeared to be in turn an important factor affecting phytoplankton richness (Table 3). Our evidence may reinforce the role of temperature as one of the main drivers of plankton richness patterns (Stomp et al. 2011), and reflect the presence of a linkage in food-webs between the diversity of predators and preys (Longmuir et al. 2007).

Our results also point towards climatic effects (environ-mental variability in physical conditions and resources) as the main factor affecting the temporal stability (in terms of intensity of fluctuations) of plankton communities in Lake Zurich. Richness levels, previously indicated as being able to enhance ecosystem stability (Ptacnik et al. 2008, Grman et al. 2010, Hillebrand and Matthiessen 2010), were weakly associated with decreased fluctuations in phytoplankton biovolumes and zooplankton density (Table 4). Combined

even the most productive single species in the community (Tilman et al. 2001). This effect has been previously reported for phytoplankton (Ptacnik et al. 2008) and is expected to be stronger in heterogeneous than homogenous environ-ments (Loreau et al. 2003). Experimental work using phy-toplankton in homogeneous vs heterogeneous environments emphasised that this type of insurance occurs when species have fundamentally different niches (Weis et al. 2008). The accrual in phytoplankton taxonomic and functional diver-sity along with increasing lake heterogeneity by depth may have contributed to the observed trend in phytoplankton total biovolume, despite re-oligotrophication of Lake Zurich (Livingstone 2003, Anneville et al. 2004).

Interacting spatio-temporal drivers of plankton trends

Climatic factors are likely to be the starting drivers of spatial patterns in the availability of plankton resources. The imple-mentation of local policies in the control of nutrient loads triggered the re-oligotrophication process, which stabilised after the climatic warming of the North Atlantic Oscillation (NAO) (Anneville et al. 2005). In Lake Zurich, warming has impacted mixing processes, resulting in a strong tem-poral increase in water-column stability and a firmer verti-cal spatial structure of the lake (Livingstone 2003). Thermal stratification is also sensitive to changes in the structure and composition of aquatic food webs (Mazumder et al. 1990). Variation in both algal biomass and dissolved organic carbon (DOC) are important determinants of the vertical distribu-tion and depth penetration of short-wave radiation (Karlsson et al. 2009, Rinke et al. 2010). In addition, previous studies have demonstrated that experimental manipulations of food web structure can indirectly affect algal biomass, DOC com-position, and thermal stratification (Mazumder et al. 1990, Harmon et al. 2009). Regardless of the underlying mecha-nisms that shape stratification patterns, reduced mixing can produce a situation where the upper water column (photic zone) is depleted in nutrients with available resources locked away in deeper waters. This can result in steeper resource

Table 4. Multiple linear regression assessing the relative effects of average plankton richness and environmental time-variability on the stabil-ity of phytoplankton biovolume and zooplankton density. Temporal variability (SD) and average richness were calculated within a sliding five-year long temporal window applied to de-seasonalised residuals and to raw data, respectively. Effect sizes were standardised and the strongest and most significant effects are highlighted in bold.

Temporal variability (TV) in

Phytoplankton biovolume Zooplankton abundance

Effects p-values Effects p-values

(Intercept) 1.10382 0.00000 1.89762 0.00000Average phytoplankton richness 0.31330 0.00003 0.09278 0.42339Average richness of Reynolds gr. 0.34261 0.00000 0.06554 0.45213Average zooplankton richness 0.34290 0.00000 0.37830 0.00030TV in temperature 0.79457 0.00000 0.89698 0.00000TV in conductivity 0.12832 0.08721 0.40669 0.00032TV in pH 0.66390 0.00000 0.67146 0.00000TV in P-PO4

3 1.40557 0.00000 0.62175 0.00229TV in N-NO3

0.40469 0.00079 1.16218 0.00000TV in light abs (254 nm) 0.08407 0.20405 0.17111 0.08805TV in zooplankton abundance 0.33432 0.00000 \ \TV in phytoplankton biovolume \ \ 0.76937 0.00000

9

et al. 2010). A key feature allowing species coexistence becomes the period and the amplitude of resource fluctua-tions, which appear to optimize species coexistence at inter-mediate values (Chesson 2000, Huisman 2010). In the Lake Zurich plankton community, temporal stability of physical-chemical conditions and P-PO4

3, combined with fluctu-ating availability of other resources (Fig. 2, Supplementary material Appendix 2) may have concurred in allowing spe-cies coexistence and a net gain in diversity with time.

In our study, however, heterogeneity by depth in environ-mental physical conditions and P-PO4 appeared to be the most important correlate of the increase in phytoplankton species and functional richness (Table 3). Functional phyto-plankton groups differ in their capability for migration and physiological aspects related to nutrient uptake and light harvesting (Reynolds et al. 2002), so that their optima would correspond to different parts of the underwater resource spec-trum. Abiotic spatial variability can facilitate species coexis-tence via a number of processes (Chesson 2000, White et al. 2010). Heterogeneity of resources among different depths can be a determinant factor allowing niche-partitioning and stable species coexistence (Longhi and Beisner 2009, Yoshiyama et al. 2009). Theoretical work has proposed that spatial heterogeneity of resources is potentially capable of allowing coexistence of competitors by reducing spatial niche overlap (Shurin et al. 2004). Climate warming and lake internal feedbacks, resulting in stronger water column stability, may have allowed the distribution of phytoplank-ton into more defined layers over the vertical gradient, hence contributing to the accrual in richness that we observed. In this case, we would observe phytoplankton assemblages becoming more depth-specific (more dissimilarity appear-ing in community composition between adjacent depths) with increasing time in Lake Zurich. We found preliminary support for this hypothesis: Jaccard dissimilarity in phyto-plankton community composition between adjacent depths in Lake Zurich was significantly higher in later periods of the time-series (Supplementary material Appendix 5). An alternative neutral view may also be valid, with reduced mix-ing of the water layers resulting in limited local dispersal, which may favour spatial heterogeneity of organisms and an increase in total plankton richness over the water column (Hubbell 2001). We are currently investigating the vertical (depth) patterns in phytoplankton species co-occurrence and testing specific niche and neutral hypotheses.

The maintenance and the relative rate of change in diversity and functionality of an ecosystem may strongly depend on whether the associated mechanisms are driven by purely stochastic (neutral) or deterministic (niche) processes (Chesson 2000, Hubbell 2001, Chase and Leibold 2003). The directionality in environmental forcing, spatio-temporal heterogeneity and taxonomic/functional group richness in Lake Zurich would indicate deterministic effects, i.e. that new niches became available in the lake. Different phyto-sociological associations in Lake Zurich were different in key functional traits, hence occupy different niches (Reynolds et al. 2002). Besides the arguments discussed above, the discrepancy between linear slopes of taxonomic and functional richness (Fig. 3, Table 2) may suggest that, within functional groups, different species share similar niches on the basis of equalising mechanisms or near neutral interactions (Scheffer

effects of warming and oligotrophication can affect the intensity of fluctuations in environmental physical conditions and resources. Previous work has suggested that warming of the upper water column of lakes, and therefore increased thermal stability, would result in a reduced frequency and/or intensity of mixing events (Livingstone 2003, Adrian et al. 2009). Reduced mixing due to lower temporal vari-ability in temperature would alter the balance between light limited algal growth and sedimentation losses affecting phy-toplankton biomass and community composition (Jäger et al. 2010), which appeared to be one of the main factor affecting zooplankton temporal variability (Table 4). Climatic-driven changes in the intensity of water column mixing can also influence resources other than light, i.e. nutrient availability (Jeppesen et al. 2005).

Spatio-temporal mechanisms favouring plankton richness

An increase in plankton species richness is expected with a decrease in eutrophication, since maximum richness is thought to be highest at intermediate productivity levels (Dodson et al. 2000, Mittelbach et al. 2001). In high nutri-ent environments biodiversity is supposed to be low because of intense competition for light, whereas at lower nutrient levels competition for light and nutrients allow for greater species coexistence (Mittelbach et al. 2001, Hautier et al. 2009). The hump shaped relationship between productivity– biodiversity predicts that species richness of plankton should increase moving from a eutrophic to a mesotrophic nutrient state. Theoretical models show that species which tradeoff light versus nutrient competitive abilities can stably co-exist in a stratified water column, depending on physical chemical conditions like mixing depth, light attenuation and nutrient input (Yoshiyama et al. 2009). Therefore both spatial and temporal features of the environment might explain the bio-diversity patterns in Lake Zurich.

The combined effects of temperature and nutrients may not stem directly from average levels of these ‘master’ driv-ers, but from their variability over time and depth. Previ-ous work has identified temporal stability in environmental physical conditions as an important factor in shaping plank-ton species coexistence patterns (Adler and Drake 2008, Barton et al. 2010, Shurin et al. 2010). Global modelling of ocean phytoplankton has also identified the stability of environmental physical conditions as an explanation for the high-diversity of phytoplankton communities (Barton et al. 2010). Conversely Shurin and co-workers (Shurin et al. 2010) have reported positive effects of fluctuating thermal conditions on zooplankton diversity across different lakes. Barton’s model assumes, however, that all plankton species are equally strong competitors (i.e. neutrality sensu Hubbell 2001). This results in high plankton biodiversity in stable environments where none of the species can outcompete the others. In contrast, when species differ in competitive abili-ties (non-neutrality), biodiversity is predicted to be greater under fluctuating conditions (Huisman 2010).

Heterogeneity in resources is extremely relevant to high biodiversity environments, by allowing spatial and tem-poral niche partitioning or stable coexistence of competitors (Colwell and Rangel 2009, Longhi and Beisner 2010, White

10

Buergi, H. R. et al. 2003. Long-term changes in functional properties and biodiversity of plankton in Lake Greifensee (Switzerland) in response to phosphorus reduction. – Aquat. Ecosyst. Health Manage. 6: 147–158.

Bukaveckas, P. A. and Robbins-Forbes, M. 2000. Role of dissolved organic carbon in the attenuation of photosynthetically active and ultraviolet radiation in Adirondack lakes. – Freshwater Biol. 43: 339–354.

Chase, J. M. and Leibold, M. A. 2003. Ecological niche. – Univ. of Chicago Press.

Chesson, P. 2000. Mechanisms of maintenance of species diversity. – Annu. Rev. Ecol. Syst. 31: 343–366.

Christensen, M. R. et al. 2006. Multiple anthropogenic stressors cause ecological surprises in boreal lakes. – Global Change Biol. 12: 2316–2322.

Cleveland, R. B. et al. 1990. STL: a seasonal-trend decomposition procedure based on Loess. – J. Official Stat. 6: 3–73.

Colwell, R. K. and Rangel, T. F. 2009. Hutchinson’s duality: the once and future niche. – Proc. Natl Acad. Sci. 106: 19651–19658.

Diaz, S. et al. 2006. Biodiversity loss threatens human well-being. – PLoS Biol. 4.

Dodson, S. I. et al. 2000. The relationship in lake communities between primary productivity and species richness. – Ecology 81: 2662–2679.

Folke, C. et al. 2004. Regime shifts, resilience and biodiversity in ecosystem management. – Annu. Rev. Ecol. Evol. Syst. 35: 557–581.

Fukami, T. 2010. Community assembly dynamics in space. – In: Verhoef, H. A. and Morin, P. J. (eds), Community ecology: processes, models and applications. Oxford Univ. Press, pp. 45–54.

Grman, E. et al. 2010. Mechanisms contributing to stability in ecosystem function depend on the environmental context. – Ecol. Lett. 13: 1400–1410.

Harmon, L. J. et al. 2009. Evolutionary diversification in stickleback affects ecosystem functioning. – Nature 458: 1167–1170.

Hautier, Y. et al. 2009. Competition for light causes plant biodi-versity loss after eutrophication. – Science 324: 636–638.

Hillebrand, H. and Matthiessen, B. 2009. Biodiversity in a com-plex world: consolidation and progress in functional biodiver-sity research. – Ecol. Lett. 12: 1405–1419.

Hooper, D. U. et al. 2005. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. – Ecol. Monogr. 75: 3–35.

Hubbell, S. P. 2001. The unified neutral theory of biodiversity and biogeography. – Princeton Univ. Press.

Huisman, J. 2010. Comment on “Patterns of diversity in marine phytoplankton”. – Science 329: 512c.

Ibelings, B. W. et al. 2007. Resilience of alternative stable states during the recovery of shallow lakes from eutrophication: Lake Veluwe as a case study. – Ecosystems 10: 4–16.

Jäger, C. G. et al. 2010. Physical determinants of phytoplankton production, algal stoichiometry, and vertical nutrient fluxes. – Am. Nat. 175: E91–E104.

Jeppesen, E. et al. 2005. Lake responses to reduced nutrient load-ing - an analysis of contemporary long-term data from 35 case studies. – Freshwater Biol. 50: 1747–1771.

Karlsson, J. et al. 2009. Light limitation of nutrient-poor lake eco-systems. – Nature 460: 506–509.

Korhonen, J. J. et al. 2010. A quantitative analysis of temporal turnover in aquatic species assemblages across ecosystems. – Ecology 91: 508–517.

Legendre, P. and Legendre, L. 1998. Ecological data series. – In: Legendre, P. and Legendre, L. (eds), Numerical ecology. Else-vier, pp. 637–705.

Livingstone, D. M. 2003. Impact of secular climate change on the thermal structure of a large temperate central European lake. – Climatic Change 57: 205–225.

and Van Nes 2006, Adler et al. 2007). Neutral and niche-based processes combine to generate coexistence and may together provide a better model for understanding plankton diversity patterns (Scheffer and Van Nes 2006, Adler et al. 2007).

Conclusions

We observed a strong accrual in plankton richness in a rare time-series of lake biodiversity data spanning more than 30 years, during a period of multiple anthropogenic impacts. Arguably, eutrophication and global warming are the two main drivers of change in aquatic ecosystems worldwide. Anthropogenic-driven environmental change can have profound detrimental effects in aquatic ecosystems, and whether management strategies are capable of fully restor-ing the health of water resources is still an open question. Our work shows that phosphorus control during climate-warming favours the accrual of both structural diversity in terms of species richness and functional associations in a deep and temperate lake. This advances a crucial contemporary debate on the response of diversity to anthropogenic environ-mental change. In natural plankton assemblages, simultane-ous changes in temporal trends and spatial (depth) variability of environmental physical conditions and resources appeared to result in a net gain in plankton community richness. Although our study focuses on a single lake, the responses we have documented may be common at least throughout lakes within the European peri-alpine climatic region (Buergi and Stadelmann 2002, Buergi et al. 2003, Anneville et al. 2005).

Acknowledgments – We would like to thank O. Koster and R. Forster (WVZ) for providing access and valuable insights to the Lake Zurich dataset. We are grateful to P. C. Hanson for the fruitful discussions emerged during GLEON meetings. FP was supported by Eawag action field project Aquaprobe.

References

Adler, P. B. and Drake, J. M. 2008. Environmental variation, sto-chastic extinction, and competitive coexistence. – Am. Nat. 172: E186–E195.

Adler, P. B. et al. 2007. A niche for neutrality. – Ecol. Lett. 10: 95–104.

Adrian, R. et al. 2009. Lakes as sentinels of climate change. – Lim-nol. Oceanogr. 54: 2283–2297.

Anneville, O. et al. 2004. Seasonal and inter-annual scales of vari-ability in phytoplankton assemblages: comparison of phyto-plankton dynamics in three peri-alpine lakes over a period of 28 years. – Freshwater Biol. 49: 98–115.

Anneville, O. et al. 2005. Phosphorus decrease and climate variabil-ity: mediators of synchrony in phytoplankton changes among European peri-alpine lakes. – Freshwater Biol. 50: 1731–1746.

Barnett, A. J. et al. 2007. Functional diversity of crustacean zoo-plankton communities: towards a trait-based classification. – Freshwater Biol. 52: 796–813.

Barton, A. D. et al. 2010. Patterns of diversity in marine phyto-plankton. – Science 327: 1509–1511.

Buergi, H. R. and Stadelmann, P. 2002. Change of phytoplankton composition and biodiversity in Lake Sempach before and during restoration. – Hydrobiologia 469: 33–48.

11

Rockström, J. et al. 2009. A safe operating space for humanity. – Nature 461: 472–475.

Scheffer, M. and Van Nes, E. H. 2006. Self-organized similarity, the evolutionary emergence of groups of similar species. – Proc. Natl Acad. Sci. USA 103: 6230–6235.

Shen, G. et al. 2009. Species–area relationships explained by the joint effects of dispersal limitation and habitat heterogeneity. – Ecology 90: 3033–3041.

Shurin, J. B. et al. 2004. Alternative stable states and regional com-munity structure. – J. Theor. Biol. 227: 359–368.

Shurin, J. B. et al. 2010. Environmental stability and lake zoo-plankton diversity - contrasting effects of chemical and thermal variability. – Ecol. Lett. 13: 453–463.

Smith, V. H. and Schindler, D. W. 2009. Eutrophication science: where do we go from here? – Trends Ecol. Evol. 24: 201–207.

Stomp, M. et al. 2011. Large-scale biodiversity patterns in fresh-water phytoplankton. – Ecology in press.

Strayer, D. L. and Dudgeon, D. 2010. Freshwater biodiversity con-servation: recent progress and future challenges. – J. N. Am. Benthol. Soc. 29: 344–358.

Suding, K. N. et al. 2008. Scaling environmental change through the community-level: a trait-based response-and-effect frame-work for plants. – Global Change Biol. 14: 1125–1140.

Tilman, D. et al. 2001. Diversity and productivity in a long-term grassland experiment. – Science 294: 843–845.

Tittensor, D. P. et al. 2010. Global patterns and predictors of marine biodiversity across taxa. – Nature 466: 1098–1101.

Van Donk, E. et al. 2003. Climate warming causes regime shifts in lake food webs: a reassessment. – Limnol. Oceanogr. 48: 1350–1353.

Weis, J. J. et al. 2008. Effects of algal diversity on the production of biomass in homogeneous and heterogeneous nutrient envi-ronments: a microcosm experiment. – PLoS One 3.

White, E. P. et al. 2010. Integrating spatial and temporal approaches to understanding species richness. – Phil. Trans. R. Soc. B 365: 3633–3643.

Yoshiyama, K. et al. 2009. Phytoplankton competition for nutri-ents and light in a stratified water column. – Am. Nat. 174: 190–203.

Longhi, M. L. and Beisner, B. E. 2009. Environmental factors controlling the vertical distribution of phytoplankton in lakes. – J. Plankton Res. 31: 1195–1207.

Longhi, M. L. and Beisner, B. E. 2010. Patterns in taxonomic and functional diversity of lake phytoplankton. – Freshwater Biol. 55: 1349–1366.

Longmuir, A. et al. 2007. Independent gradients of producer, con-sumer, and microbial diversity in Lake Plankton. – Ecology 88: 1663–1674.

Loreau, M. et al. 2003. Biodiversity as spatial insurance in heterogeneous landscapes. – Proc. Natl Acad. Sci. USA 100: 12765–12770.

Magurran, A. E. et al. 2010. Long-term datasets in biodiversity research and monitoring: assessing change in ecological com-munities through time. – Trends Ecol. Evol. 25: 574–582.

Mazumder, A. et al. 1990. Effects of fish and plankton and lake temperature and mixing depth. – Science 247: 312–315.

Mittelbach, G. G. et al. 2001. What is the observed relationship between species richness and productivity? – Ecology 82: 2381–2396.

O’Brien, R. M. 2007. A caution regarding rules of thumb for variance inflation factors. – Qual. Quant. 41: 673–690.

Padisák, J. et al. 2009. Use and misuse in the application of the phytoplankton functional classification: a critical review with updates. – Hydrobiologia 621: 1–19.

Paerl, H. W. and Huisman, J. 2008. Blooms like it hot. – Science 320: 57–58.

Peeters, F. et al. 2002. Modeling 50 years of historical temperature profiles in a large central European lake. – Limnol. Oceanogr. 47: 186–197.

Ptacnik, R. et al. 2008. Diversity predicts stability and resource use efficiency in natural phytoplankton communities. – Proc. Natl Acad. Sci. USA 105: 5134–5138.

Reiss, J. et al. 2009. Emerging horizons in biodiversity and ecosys-tem functioning research. – Trends Ecol. Evol. 24: 505–514.

Reynolds, C. S. et al. 2002. Towards a functional classification of the freshwater phytoplankton. – J. Plankton Res. 24: 417–428.

Rinke, K. et al. 2010. A simulation study of the feedback of phy-toplankton on thermal structure via light extinction. – Fresh-water Biol. 55: 1674–1693.

Supplementary material (available as Appendix O20055 at www.oikosoffice.lu.se/appendix). Appendix 1–5.