Chapter 2: Scientific background
This chapter will give a brief introduction to the scientific background relevant for the following chapters. The topics covered are phytoplankton ecology, nutrient cycling in lakes, the influence of climate change on lakes and hydrodynamic-ecological simulation of lakes.
Chapter 3: Use of a weather generator for simulating climate change effects on ecosystems: A case study on Lake Constance
The focus of this chapter is on the introduction and application of a new vector-autoregressive moving average weather generator (VG). The weather generator allows us to produce syn-thetic meteorological time series based on long-term meteorological observations from the past. During the generation process, disturbances can be added, e.g. an increase in mean air temperature. The time series of a changed climate can thus be generated and used as forcing for a lake model. Here, I tested the sensitivity of a large, monomictic lake to changes in mean air temperature, as well as changes in variability and a combination of both.
Chapter 4: Meteorological control of lake ecosystems: The (un)importance of air temperature
This chapter questions the strong bias of many climate impact studies towards changes in air temperature. The weather generator VG was used to produce multiple time series of the current climate. Results of the lake simulations were analysed to gain insight into which me-teorological patterns cause stratification and phytoplankton spring bloom onset. The relative importance of different meteorological variables was assessed. The study further addresses the question of the time scales on which meteorological variables have their impact on the cardinal dates stratification onset and bloom onset.
Chapter 5: Predictive utility of trait-separated phytoplankton groups: A robust approach to modeling population dynamics
Unlike the other chapters, this chapter applies the ecological lake model PROTECH (Phyto-plankton RespOnses To Environmental CHange). Phyto(Phyto-plankton succession is quantitatively described based on morphological and physiological traits. In a very generalised approach, the model was run for hypothetical lakes with different depths and located in different cli-mates. Phytoplankton succession was analysed based on the sensitivity of traits in response to a range of environmental settings.
Chapter 6: Algal internal nutrient stores feedback on vertical phosphorus distribution in large lakes
This chapter compares two structurally different methods for the quantification of nutrient-dependent phytoplankton growth: The static P model prescribed a fixed cell stoichiometry, while the dynamic P model allowed for a flexible cell stoichiometry and thus enabled lux-ury uptake. The chapter evaluates how luxlux-ury uptake and internal nutrient storage affect phytoplankton dynamics and the spatial distribution of phosphate within lakes.
Chapter 7: General discussion
The final chapter summarises the results and discusses their implications, with an outlook for further research ideas.
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Scientific background
2.1 Phytoplankton ecology
Phytoplankton is one of the simpler forms of life on earth and yet thousands of different species exist in marine and inland waters (Reynolds,2006) with a diverse range of morphological and functional traits. Phytoplankton by definition is photoautotrophic. As a primary producer of carbon it is therefore located at the base of the lake food web and serves other trophic levels by the allocation of energy. This position assigns phytoplankton a key role in the food web and changes in phytoplankton phenology and composition will affect the whole lake ecosystem.
In the 1980s the plankton ecology group (PEG) model was developed (Sommer et al., 1986), a conceptual model describing the seasonal development of phyto- and zooplankton in both eutrophic and oligotrophic lakes in the temperate zone. Nowadays, many studies refer to the PEG model and it is an inherent aspect of limnological textbooks (e.g. Lampert &
Sommer,1999;Wetzel,2001). Several papers have corroborated the single phases described in the model. In late winter/early spring, abiotic forcing prevails and phytoplankton growth is limited by light availability (e.g. Sverdrup, 1953;Talling,1971). When the lake begins to stratify, a phytoplankton bloom develops (Huisman et al., 1999), mainly composed of light-tolerant fast-growing species (Reynolds et al., 2002). This intense growth brings about the depletion of nutrients, especially phosphorus. Additionally, grazing pressure by zooplankton increases (Tirok & Gaedke, 2007b). Both factors lead to a breakdown of the phytoplankton bloom (‘clear-water phase’, e.g. Winder & Schindler, 2004b). Subsequently, algae develop with a greater resistance to grazing pressure and a higher ability to compete for nutrients (Annevilleet al.,2002). With the beginning of autumnal turnover, light becomes the limiting factor again and hampers phytoplankton growth. The PEG model concept was later refined and extended to other climatic zones (Sommer et al.,2012).
Phytoplankton has been classified in various ways (Anneville et al.,2002;Reynoldset al., 2002; König-Rinke, 2008;Mieleitner et al., 2008). Obvious classification criteria include size classes (femto-, pico- and nanoplankton, seeSieburthet al.,1978) or taxonomy (families and
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species, Reynolds, 2006). These classifications, however, are not very useful when studying the function of phytoplankton. The former is too unilateral, while the latter is too detailed and presents the problem of overdispersion (Kruk et al., 2010). Overdispersion means that phylogenetic close species occupy very different niches (Webb et al., 2002). This is why attempts have been made to describe phytoplankton based on characteristics determining their function and position in the aquatic ecosystem. Two alternative approaches divide phytoplankton into functional groups (Reynoldset al.,2002;Mieleitneret al.,2008) or describe a community based on its trait composition (Litchman & Klausmeier, 2008). Litchman &
Klausmeier(2008) offers an instructive overview of phytoplankton traits by dividing the latter into different types and their various levels of importance for different ecological functions (see their Fig.1). Functional group and trait-based phytoplankton classifications are very useful for aquatic modellers. They allow the modeller to reduce the number of simulated groups to relatively few while at the same time keeping the model flexible enough to react to changing conditions within the lake. The different classification approaches are mirrored in the diverse range of phytoplankton models (Mooijet al.,2010;Rigosi et al.,2010).