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.
The aim of the internship was to understand the potential effects of high and middle intensity weather events (such as storms and floods) on the functional traits of phytoplankton communities living in the lake using an experimental approach.
In situ mesocosms have been used for climate change research since 1995 (Stewart et al., 2013). This approach enables to isolate some parts of the lake and manipulate directly some parameters of the ecosystem. It is thus a good compromise between laboratory experiments and field surveys. The mesocosm size (i.e. > 1000 L) enables to experiment at a broader scale compared to microcosms. For instance, it allows investigating at the community level the direct effects of climate change, such as changes of species phenology and the indirect effect such as trophic and non-trophic interactions (Jenny et al., 2020).
The main questions of this internship were to understand to what extent extreme weather events (as storms, floods) impact on the compositions of phytoplankton communities in a large pre-alpine lake.
The main hypotheses were:
1. The biodiversity of phytoplankton would be reduced with storms of higher frequency and intensity and be replaced by generalist organisms.
2. The impacts would be more visible on storms of higher intensity within a short time than on storms with higher frequency on a longer time.
3. It is assumed that ecosystems can be resilient. Therefore, we expected the effects of different storms lasting for a short time.
The first part of this report focuses on the description of the mesocosm experiment that was run in 2019 and the methodology to assess phytoplankton communities. In the results, the main trends concerning physico-chemical parameters and phytoplankton communities are presented, followed by the interpretation of the results and a final part discussing the limits of the study.
Material and method
Mesocosm experimental design
The mesocosm experiment was conducted during July 2019 to simulate the effect of predicted scenarios of extreme climate events on natural plankton communities. Nine pelagic mesocosms (about 3000 L, 3 m depth) were deployed near the shore of Thonon-les-Bains (France) in Lake Geneva.
The design of the experiment consisted of three treatments each replicated three times: a control treatment (named C – no treatment applied) and two different treatments simulating different intensities of weather events. A medium intensity treatment (M) aiming at reproducing less intense and more prolonged events and a high intensity treatment (H) aiming at reproducing short and intense weather events such as violent storms.
In order to simulate these weather conditions, three main parameters were modified:
• Dissolved Organic Carbon: DOC concentration was increased by adding a solution prepared by extracting commercially available bio-peat soil (bought from the Belgian company DCM). 150 g of peat soil was mixed with 1.5 L of distilled water and autoclaved for one hour at 120 °C. This solution was then centrifuged for 15 min at 3500 r/min and the supernatant filtered through a 0.7 µm glass fiber filter. The filtrate was again autoclaved for sterilization before it was added to the mesocosms. Different volumes of the solution were added to the different treatments as described in Table 1.
• Light: incident irradiance was reduced using vinyl filters (bought from the American company LEE Filters). Those filters were placed at the top of the mesocosms (Figure 2) with different opacity as described in Table 1.
• Mixing: a top-bottom current in the mesocosm water column was created by manual mixing performed by lowering and lifting a three-meter-long stick with a drilled disk.
The mesocosms were arranged randomly forming a Latin square (Figure 3). They consisted of polypropylene reinforced bags (produced by Insinööritoimisto Haikonen Oy, Finland) of about three meters depth and one-meter wide ending as a cone (about 3000 L volume). All the mesocosm bags were filled passively with lake water the same day within few hours and left to acclimate for three days before the start of the experiment. The experiment lasted in total four weeks (July 4 to 30), the high intensity treatment (H) consisted of a short-term intense stress applied for 5 days during the first week (from July 4 to 8). After this period, the H treatment mesocosms were exposed to the control conditions (covered with a 95% transmitted light filter, no further DOC increase and no mixing). The medium intensity treatment (M) was maintained for 4 weeks. In this work, we present the results from the first 2 weeks of the experiment (July 4 to 16, 4 samplings), to focus more on the effect and responses of the phytoplankton community to the high intensity treatment.
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).
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).
Counting of phytoplankton
A Zeiss Axiovert 135 microscope was used at 40*1.6 magnification. The interference contrast (DIC 5.1A) was used for the phytoplankton identifications (Figure 6), but the phase contrast could be also used (Figure 7). The Cell_P software (Olympus Soft Imaging Solutions, Germany) with an Olympus DP71 (camera) was used to visualize phytoplankton on a computer screen and take pictures.
For phytoplankton counting I followed the standard procedure (NF EN 15.204 from 2006) congruent with Utermöhl’s methodology. The method consists in counting at least 400 individuals or “algal object”. It is defined as one or several cells forming an independent group regarding other particles of the sample. The definition depends on the organism shape: whether it is filamentous or unicellular. Some common rules have been established (Annex 1). For instance, a filamentous individual is accounted every 100µm, that is to say a quarter of the ocular field whereas colonies such as Aphanocapsa delicatissima are accounted as one individual from 10 µm. Only viable cells (i.e. concerning diatoms the frustules containing plastids) were counted, except for the Dinobryon for which the lorica were counted.
It has been sometimes laborious to count until 400 individuals requiring up to five times more ocular fields than usual. There are few solutions to avoid this issue such as lowering the number of individuals to count or using a bigger sedimentation chamber (one of 50 mL for example that enables a larger amount of phytoplankton to sediment). The last solution has some drawbacks regarding mainly the volume of the collected sample (100 mL) that reduces the number of slides to be observed under the microscope in case of leaks. Besides, a larger tube increases the instability of the chamber on the slide.
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
2.1 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
3.1 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