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Global changes affecting lake services
Lakes provide through their processes fundamental ecosystem services to humankind: resource provisioning (drinking water, fishery), cultural (leisure activities, tourism), regulating services (water regulation, carbon sequestration, substrate for biodiversity). Understanding better the underpinning biodiversity and processes in the lakes is essential to forecast and manage these ecosystems under pressure.
Local and global forcings are causing changes of abiotic and biotic features of the lakes and pose risks for the services the lakes provide.
Temperature rises affect directly meteorological events such as heatwaves and climate extremes (IPCC, 2012). Depletion of oxygen has been seen in lakes because of temperature rises and releases of phosphorus from sediments in the hypolimnion (Wilhelm & Adrian, 2008). In 2003, high heat peaks caused a decrease of oxygen in Swiss lakes (Jankowski et al, 2006). The scientists argue that heatwaves implying anoxic conditions in deep water is a major issue of non-anthropogenic eutrophication. Temperature rises cause also a thermal stratification of the water column preventing nutrients to reach higher parts of the water column, which has a further impact on the biodiversity.
Moreover, the higher intensity and frequency of extreme events (IPCC, 2014) can change strongly the lake conditions. In fact, record-breaking raining events have been studied over the past century showing a higher frequency in Europe: +31% from 1980 to 2010 compared to expectations of rainy events in a steady climate (Lehmann et al., 2015). In another part of the world, extreme precipitation tends to intensify, as for Lake Victoria, where extreme rainfalls are predicted to double over the lake by the end of the century (Thiery et al., 2016). Storms, as the merger of heavy rains and heavy winds (Easterling et al., 2000), alter the physical parameters of lakes such as light availability, nutrient mixing, temperature distribution in the water column. In particular, heavy rains strengthen the inputs of organic matter and heavy winds intensify water column mixing and thus affect the turbidity of water (Stockwell et al., 2020).
These shifts on the abiotic features of the lakes have consequences on the aquatic trophic networks. From a rise of heterotrophic bacteria (Rasconi et al., 2015) to the collapse of cold-water fishes along with overfishing pressure (Jenny et al., 2020), monitoring lakes is crucial in order to better manage them.
Phytoplankton as bioindicators of the global changes
Phytoplankton constitutes the basis of the trophic chain and plays a major role in lake ecosystem services as absorbing carbon dioxide and providing dioxygen for other organisms. As having a short life cycle and a fast turnover, phytoplankton reacts rapidly to external forcing and altered ecosystem processes. It is thus studied as an indicator of the ecological quality of lakes at large scale according to the European Water Framework Directive (WFD) and for instance its main application is as an indicator of eutrophication (Thackeray et al., 2013).
The rise of temperature causing a depletion of nutrients impacts directly on phytoplankton communities. Larger phytoplankton such as diatoms, that require higher amount of nutrients, tend to be disadvantaged, contrary to smaller ones such as cyanobacteria (Bopp et al, 2005).
Storm events cause a decrease of light availability and nutrient mixing that are key determinants for phytoplankton growth (Stockwell et al., 2020). In this quoted review, it has been demonstrated that windy and rainy events lead to a decrease of phytoplankton biomass, whereas chlorophyll a remains stable. Thus, there are changes in phytoplankton assemblages. According to the CSR strategy (Reynolds, 1988), ruderal species would survive storm events due to their higher intrinsic growth rate, whereas competitive species are dominating in stable environment (high nutrient loads and light) and coming later in ecological succession (Altermatt et al., 2011). According to the intermediate disturbance hypothesis (IDH) (Connell, 1978), it is assumed that the maximum diversity would be reached at a medium stage of disturbance. Species with efficient uptake ratio would outcompete other species at low-level storm event, whereas ruderal species would exploit the internal and external nutriment loads and dominate the ecosystem.
Functional groups as a simplified way to assess phytoplankton communities
There are several ways to categorize phytoplankton because they have different morphologies (filamentous, circular, colonies) and characteristics that confer them many functions (N-fixing for some cyanobacteria, flagella and gas-vesicles for buoyancy regulators and motile species…). An interesting way to study the ecology of phytoplankton communities is to classify them into functional groups. Over the past decades, scientists proposed different classifications. For instance, Reynolds suggested first in the late eighties the CSR concept (Competitor, Stress-tolerant and Ruderal) that is close to the ‘r’ and ‘K’ strategists classification of MacArthur and Wilson (MacArthur & Wilson, 1967). Some species are more adapted to spread fast and colonize new environment (‘r’ and ruderal species proliferating in meso-eutrophic lakes), whereas some other species are more specialized and competitive to grow in stable environments, for example having structures for efficient uptake of nutrients (‘K’, as competitor species adapted to oligotrophic lakes). In parallel, Reynolds proposed a classification in 31 functional groups representative of different habitats (eutrophic state, mixing water…) and range of tolerance (nutrient deficiency, level of light, pH…) (Reynolds et al., 2002). This classification has then been revised by three scientists into 41 functional groups improving accuracy of descriptions and misplacements of some species by other authors (Padisák et al., 2009). Classification into functional groups has simplified the monitoring by environmental agencies (Padisák et al., 2009) and constitutes a helpful tool for the Water Framework Directive.
Physico-chemical parameters
Physico-chemical characterisation of each mesocosm included in situ measures of temperature, pH, conductivity, oxygen, redox potential and turbidity using a multiparameter probe (YSI EXO1) and light spectral measurements using a RAMSES-ASC-VIS irradiance sensor. Organic matter (total organic carbon, TOC), and nutrients (P, N, Si) were measured by laboratory analysis (all the physico-chemical parameters are presented in Annex 3).
Phytoplankton community
Samples for phytoplankton community characterization were taken in every mesocosm at 2 m depth using a Niskin bottle. 100 ml of raw sample were immediately fixed by adding 5 ml of Lugol (iodine solution) according to the INRAE protocol (Druart & Rimet, 2008), which is also in agreement with the protocol used in the context of the Water Framework Directive (CEMAGREF & INRA, 2009) and follows the Utermöhl technique (Utermöhl, 1958) which has been standardised and European level (CEN, 2006). The amber color given by Lugol, enables certain organisms to be more visible. The samples were labelled with the corresponding day of the sample (S1, S2, S3 and S4) and the corresponding mesocosm (C1, C2, C3, H1, H2, H3, M1, M2, M3). The samples were preserved in the dark at 4 °C and analysed within few months.
In February 2020, I took the samples out. Each sample was gently shaken to be homogenised and then poured into a 25 mL sedimentation chamber (or Utermöhl chamber) superposed on a slide with a depression. After 12 hours, the sedimentation chamber was removed and was replaced by a cover slip. Then, the slide was examined under an inverted microscope (Figure 4, Figure 5).
Physico-chemical parameters
At the beginning of the experiment (S1) there was no significant difference in TOC concentration (Figure 10), the average was 1.3 mgCL-1 ± 0.11 in all the treatments. In the C treatment, TOC remained stable (1.33 mgCL-1 ± 0.12) during the entire experiment. In the treatment M, TOC concentration increased during the experiment (S2 and S3: 1.73 mgCL-1 ± 0.04 and 1.9 mgCL-1 ± 0.14 respectively) and was lower at the end (S4: 1.21 mgCL-1 ± 0.06). In the H treatment, there was a peak in the TOC concentration (S2: 3.97 mgCL-1 ± 0.08 i.e. 2.5 times more than in the other treatments), followed by a decrease (S3 and S4: 1.73 mgCL-1 ± 0.08 and 1.16 mgCL-1 ± 0.01 respectively).
Light (Figure 11) at the beginning of the experiment was lower in the H treatment (38.7 μmolm-2s-1) compared to M (51.4 μmolm-2s-1) and C (57.2 μmolm-2s-1). During the experiment, light was highly variable and lowest in H (S2: 40.8 μmolm-2s-1 ± 15.8), compared to C (63.1 μmolm-2s-1 ± 36.2) and M (84 μmolm-2s-1 ± 32.3). Following, a decrease was observed in all treatments and again values were lowest in H (S3: 25.6 μmolm-2s-1 ± 2.2), lower in M (30.9 μmolm-2s-1 ± 6.9) and slightly higher in C (32.9 μmolm-2s-1 ± 4). At the end of the experiment, light was lowest in M (S4: 53.9 μmolm-2s-1 ± 12.7), intermediate in H (56.7 μmolm-2s-1 ± 13.5) and highest in C (59 μmolm-2s-1 ± 7.6).
Phytoplankton functional groups
At S1, the main functional group in all the treatments was the “E” group (27% ± 8) mainly composed by the species Dinobryon. At S2, the treatment H was principally constituted by the “Unclassified” group (30% ± 13), while C and M were still represented by the “E” group (respectively 39% ± 5 and 45% ± 9). At S3, C, H and M were constituted mainly by the “X1” group (23% ± 7) and the species Monoraphidium. The “E” group was still present in the M treatment (14% ± 5). At S4, H was principally composed by the “Lo” group (51% ± 8). C was represented by the “P” group (13% ± 4) composed of diatoms such as Diatoma elongatum and Fragilaria crotonensis and the “X1” group (13% ± 5). M was composed by the “Y” group (15% ±3) represented by the species Cryptomonas and Gymnodinium, and the “X1” group (17% ± 4).
The NMDS (Figure 14) shows the distribution of the phytoplankton functional groups during the experiment. At S1, the phytoplankton community was significantly different between treatments (pvalue ≤ 0.01, PERMANOVA). Separated from the core cluster were the replicates H1 and H2 at S2. At this moment, due to the effect of the treatments, the phytoplankton community in H was significantly different from M and C (pvalue ≤ 0.01, PERMANOVA). It was mainly composed by the group “Unclassified”, constituted only by the species Desmarella brachycalyx from the class of Choanoflagellatea (even if they do not contain any chlorophyll, most of Choanoflagellatea are traditionally counted in the phytoplankton analyses). Significant differences in the phytoplankton community between S2 and S4 were due to the interaction between time and treatment (pvalue ≤ 0.001, PERMANOVA). The “Y” functional group was indicative for the M treatment (pvalue ≤ 0.01, multipatt) and differentiated M-H (pvalue ≤ 0.001, simper) and M-C (pvalue ≤ 0.01, simper). “Lo” group and “Unclassified” group were indicative for the H treatment (pvalue ≤ 0.01, multipatt), but only the “Unclassified” group significantly contributed to differences between M-H (respectively pvalue ≤ 0.01 and pvalue ≤ 0.01, simper) and C-H (respectively pvalue ≤ 0.01 and pvalue ≤ 0.01, simper).
The community turnover (Figure 15) between dates was significantly higher in H compared to C and M (pvalue = 0.000371, ANOVA), and the highest turnover was between S2-S3 (0.77 ± 0.06). In the C treatment, the community turnover was similar between S1-S2 and S2-S3 and lower between S3-S4. In the M treatment, the community turnover had the same pattern as in treatment H. The highest turnover was between S2-S3 (0.56 ± 0.05), while it was lower and similar between S1-S2 and S3-S4.
Table of contents :
1 Material and method
1.1 Mesocosm experimental design
1.2 Sample analysis
1.2.1 Physico-chemical parameters
1.2.2 Phytoplankton community
1.2.3 Counting of phytoplankton
1.2.4 Conversion from algal object counts to biovolume
1.2.5 Phytoplankton diversity and functional groups
1.3 Data analyses
2 Results
2.1 Physico-chemical parameters
2.2 Phytoplankton diversity
2.2.1 Diversity indexes
2.2.2 Phytoplankton functional groups
2.2.3 Trophic strategy of functional groups
2.3 Effect of the treatments on phytoplankton community
3 Discussion
3.1 Physico-chemical parameters
3.2 Phytoplankton community
3.2.1 Phytoplankton biovolume
3.2.2 Diversity indexes
3.2.3 Phytoplankton functional groups and turnover
3.2.4 Trophic strategy of functional groups
3.3 Effect of the manipulated environmental parameters on the phytoplankton community
Conclusion
Personal experience
Bibliography .
