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Institute
Under natural conditions, aboveground herbivory and plant-soil feedbacks (PSFs) are omnipresent interactions strongly affecting individual plant performance. While recent research revealed that aboveground insect herbivory generally impacts the outcome of PSFs, no study tested to what extent the intensity of herbivory affects the outcome. This, however, is essential to estimate the contribution of PSFs to plant performance under natural conditions in the field. Here, we tested PSF effects both with and without exposure to aboveground herbivory for four common grass species in nine grasslands that formed a gradient of aboveground invertebrate herbivory. Without aboveground herbivores, PSFs for each of the four grass species were similar in each of the nine grasslands-both in direction and in magnitude. In the presence of herbivores, however, the PSFs differed from those measured under herbivory exclusion, and depended on the intensity of herbivory. At low levels of herbivory, PSFs were similar in the presence and absence of herbivores, but differed at high herbivory levels. While PSFs without herbivores remained similar along the gradient of herbivory intensity, increasing herbivory intensity mostly resulted in neutral PSFs in the presence of herbivores. This suggests that the relative importance of PSFs for plant-species performance in grassland communities decreases with increasing intensity of herbivory. Hence, PSFs might be more important for plant performance in ecosystems with low herbivore pressure than in ecosystems with large impacts of insect herbivores.
Arthropods are incapable of synthesizing sterols de novo and thus require a dietary source to cover their physiological demands. The most prominent sterol in animal tissues is cholesterol, which is an indispensable structural component of cell membranes and serves as precursor for steroid hormones. Instead of cholesterol, plants and algae contain a variety of different phytosterols. Consequently, herbivorous arthropods have to metabolize dietary phytosterols to cholesterol to meet their requirements for growth and reproduction. Here, we investigated sterol-limited growth responses of the freshwater herbivore Daphnia magna by supplementing a sterol-free diet with increasing amounts of 10 different phytosterols and comparing thresholds for sterol-limited growth. In addition, we analyzed the sterol composition of D. magna to explore sterol metabolic constraints and bioconversion capacities. We show that dietary phytosterols strongly differ in their potential to support somatic growth of D. magna. The dietary threshold concentrations obtained by supplementing the different sterols cover a wide range (3.5-34.4 mu g mg C-1) and encompass the one for cholesterol (8.9 mu g mg C-1), indicating that certain phytosterols are more efficient in supporting somatic growth than cholesterol (e.g., fucosterol, brassicasterol) while others are less efficient (e.g., dihydrocholesterol, lathosterol). The dietary sterol concentration gradients revealed that the poor quality of particular sterols can be alleviated partially by increasing dietary concentrations, and that qualitative differences among sterols are most pronounced at low to moderate dietary concentrations. We infer that the dietary sterol composition has to be considered in zooplankton nutritional ecology to accurately assess potential sterol limitations under field conditions.
Understanding how variance in environmental factors affects physiological performance, population growth, and persistence is central in ecology. Despite recent interest in the effects of variance in single biological drivers, such as temperature, we have lacked a comprehensive framework for predicting how the variances and covariances between multiple environmental factors will affect physiological rates. Here, we integrate current theory on variance effects with co-limitation theory into a single unified conceptual framework that has general applicability. We show how the framework can be applied (1) to generate mathematically tractable predictions of the physiological effects of multiple fluctuating co-limiting factors, (2) to understand how each co-limiting factor contributes to these effects, and (3) to detect mechanisms such as acclimation or physiological stress when they are at play. We show that the statistical covariance of co-limiting factors, which has not been considered before, can be a strong driver of physiological performance in various ecological contexts. Our framework can provide powerful insights on how the global change-induced shifts in multiple environmental factors affect the physiological performance of organisms.
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.