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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.
Indoor mesocosm experiments were conducted to test for potential climate change effects on the spring succession of Baltic Sea plankton. Two different temperature (Delta 0 A degrees C and Delta 6 A degrees C) and three light scenarios (62, 57 and 49 % of the natural surface light intensity on sunny days), mimicking increasing cloudiness as predicted for warmer winters in the Baltic Sea region, were simulated. By combining experimental and modeling approaches, we were able to test for a potential dietary mismatch between phytoplankton and zooplankton. Two general predator-prey models, one representing the community as a tri-trophic food chain and one as a 5-guild food web were applied to test for the consequences of different temperature sensitivities of heterotrophic components of the plankton. During the experiments, we observed reduced time-lags between the peaks of phytoplankton and protozoan biomass in response to warming. Microzooplankton peak biomass was reached by 2.5 day A degrees C-1 earlier and occurred almost synchronously with biomass peaks of phytoplankton in the warm mesocosms (Delta 6 A degrees C). The peak magnitudes of microzooplankton biomass remained unaffected by temperature, and growth rates of microzooplankton were higher at Delta 6 A degrees C (mu(a dagger 0 A degrees C) = 0.12 day(-1) and mu(a dagger 6 A degrees C) = 0.25 day(-1)). Furthermore, warming induced a shift in microzooplankton phenology leading to a faster species turnover and a shorter window of microzooplankton occurrence. Moderate differences in the light levels had no significant effect on the time-lags between autotrophic and heterotrophic biomass and on the timing, biomass maxima and growth rate of microzooplankton biomass. Both models predicted reduced time-lags between the biomass peaks of phytoplankton and its predators (both microzooplankton and copepods) with warming. The reduction of time-lags increased with increasing Q(10) values of copepods and protozoans in the tritrophic food chain. Indirect trophic effects modified this pattern in the 5-guild food web. Our study shows that instead of a mismatch, warming might lead to a stronger match between protist grazers and their prey altering in turn the transfer of matter and energy toward higher trophic levels.
Over the last two decades, macroecology the analysis of large-scale, multi-species ecological patterns and processes has established itself as a major line of biological research. Analyses of statistical links between environmental variables and biotic responses have long and successfully been employed as a main approach, but new developments are due to be utilized. Scanning the horizon of macroecology, we identified four challenges that will probably play a major role in the future. We support our claims by examples and bibliographic analyses. 1) Integrating the past into macroecological analyses, e.g. by using paleontological or phylogenetic information or by applying methods from historical biogeography, will sharpen our understanding of the underlying reasons for contemporary patterns. 2) Explicit consideration of the local processes that lead to the observed larger-scale patterns is necessary to understand the fine-grain variability found in nature, and will enable better prediction of future patterns (e.g. under environmental change conditions). 3) Macroecology is dependent on large-scale, high quality data from a broad spectrum of taxa and regions. More available data sources need to be tapped and new, small-grain large-extent data need to be collected. 4) Although macroecology already lead to mainstreaming cutting-edge statistical analysis techniques, we find that more sophisticated methods are needed to account for the biases inherent to sampling at large scale. Bayesian methods may be particularly suitable to address these challenges. To continue the vigorous development of the macroecological research agenda, it is time to address these challenges and to avoid becoming too complacent with current achievements.