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Investigating the mechanisms which underlie the biomass fluctuations of populations and communities is important to better understand the processes which buffer community biomass in a variable environment. Based on long- term data of plankton biomass in Lake Constance (Bodensee), this study aims at explaining the different degree of synchrony among populations observed within two freshwater plankton groups, phytoplankton and ciliates. Established measures of temporal variability such as the variance ratio and cross-correlation coefficients were combined with first- order autoregressive models that allow estimating species interactions from time-series data. We found that predation was an important driver of the observed seasonal variability patterns in phytoplankton and ciliates, and that competitive interactions only played a subordinate role. In Lake Constance copepods and cladocerans, two major invertebrate predator groups, focus their grazing pressure at different times of the season. Model results suggested that compensatory dynamics detected in phytoplankton originate from the differential vulnerability of species to either one of these two predator groups. For ciliates model results advocated that synchrony among species occurs because ciliates tend to be vulnerable to both predator groups. Our findings underline the necessity of extending studies of community variability to multiple trophic levels because accounting for predator-prey interactions may often be more important than accounting for competitive interactions at one trophic level

1. Models aim to predict phytoplankton dynamics based on observed initial conditions and a set of equations and parameters. However, our knowledge about initial conditions in nature is never perfect. Thus, if phytoplankton dynamics are sensitive to small variations in initial conditions, they are difficult to predict. 2. We used time-series data from indoor mesocosm experiments with natural phyto- and zooplankton communities to quantify the extent to which small initial differences in the species, functional group and community biomass in parallel treatments were amplified or buffered over time. We compared the differences in dynamics between replicates and among all mesocosms of 1year. 3. Temperature-sensitive grazing during the exponential growth phase of phytoplankton caused divergence. In contrast, negative density dependence caused convergence. 4. Mean differences in biomass between replicates were similar for all hierarchical levels. This indicates that differences in their initial conditions were amplified to the same extent. Even though large differences in biomass occasionally occurred between replicates for a short time, dynamics returned to the same path at all hierarchical levels. This suggests that internal feedback mechanisms make the spring development of phytoplankton highly predictable.

1. The polyunsaturated fatty acid eicosapentaenoic acid (EPA) plays an important role in aquatic food webs, in particular at the primary producerconsumer interface where keystone species such as daphnids may be constrained by its dietary availability. Such constraints and their seasonal and interannual changes may be detected by continuous measurements of EPA concentrations. However, such EPA measurements became common only during the last two decades, whereas long-term data sets on plankton biomass are available for many well-studied lakes. Here, we test whether it is possible to estimate EPA concentrations from abiotic variables (light and temperature) and the biomass of prey organisms (e.g. ciliates, diatoms and cryptophytes) that potentially provide EPA for consumers. 2. We used multiple linear regression to relate size- and taxonomically resolved plankton biomass data and measurements of temperature and light intensity to directly measured EPA concentrations in Lake Constance during a whole year. First, we tested the predictability of EPA concentrations from the biomass of EPA-rich organisms (diatoms, cryptophytes and ciliates). Secondly, we included the variables mean temperature and mean light intensity over the sampling depth (020 m) and depth (08 and 820 m) as factors in our model to check for large-scale seasonal- and depth-dependent effects on EPA concentrations. In a third step, we included the deviations of light and temperature from mean values in our model to allow for their potential influence on the biochemical composition of plankton organisms. We used the Akaike Information Criterion to determine the best models. 3. All approaches supported our proposition that the biomasses of specific plankton groups are variables from which seston EPA concentrations can be derived. The importance of ciliates as an EPA source in the seston was emphasised by their high weight in our models, although ciliates are neglected in most studies that link fatty acids to seston taxonomic composition. The large-scale seasonal variability of light intensity and its interaction with diatom biomass were significant predictors of EPA concentrations. The deviation of temperature from mean values, accounting for a depth-dependent effect on EPA concentrations, and its interaction with ciliate biomass were also variables with high predictive power. 4. The best models from the first and second approaches were validated with measurements of EPA concentrations from another year (1997). The estimation with the best model including only biomass explained 80%, and the best model from the second approach including mean temperature and depth explained 87% of the variability in EPA concentrations in 1997. 5. We show that it is possible to predict EPA concentrations reliably from plankton biomass, while the inclusion of abiotic factors led to results that were only partly consistent with expectations from laboratory studies. Our approach of including biotic predictors should be transferable to other systems and allow checking for biochemical constraints on primary consumers.

Stage structures of populations can have a profound influence on their dynamics. However, not much is known about the transient dynamics that follow a disturbance in such systems. Here we combined chemostat experiments with dynamical modeling to study the response of the phytoplankton species Chlorella vulgaris to press perturbations. From an initially stable steady state, we altered either the concentration or dilution rate of a growth-limiting resource. This disturbance induced a complex transient response-characterized by the possible onset of oscillations-before population numbers relaxed to a new steady state. Thus, cell numbers could initially change in the opposite direction of the long-term change. We present quantitative indexes to characterize the transients and to show that the dynamic response is dependent on the degree of synchronization among life stages, which itself depends on the state of the population before perturbation. That is, we show how identical future steady states can be approached via different transients depending on the initial population structure. Our experimental results are supported by a size-structured model that accounts for interplay between cell-cycle and population-level processes and that includes resource-dependent variability in cell size. Our results should be relevant to other populations with a stage structure including organisms of higher order.