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The mineral and biochemical food quality of prey may limit predator production. This well-studied direct bottom-up effect is especially prominent for herbivore-plant interactions. Low-quality prey species, particularly when defended, are generally considered to be less prone to predator-driven extinction. Undefended high-quality prey species sustain high predator production thereby potentially increasing their own extinction risk. The food quality of primary producers is highly species-specific. In communities of competing prey species, predators thus may supplement their diets of low-quality prey with high-quality prey, leading to indirect horizontal interactions between prey species of different food quality. We explore how these predator-mediated indirect interactions affect species coexistence in a general predator-prey model that is parametrized for an experimental algae-rotifer system. To cover a broad range of three essential functional traits that shape many plant-herbivore interactions we consider differences in 1) the food quality of the prey species, 2) their competitive ability for nutrient uptake and 3) their defence against predation. As expected, low food quality of prey can, similarly to defence, provide protection against extinction by predation. Counterintuitively, our simulations demonstrate that being of high food quality also prevents extinction of that prey species and additionally promotes coexistence with a competing, low-quality prey. The persistence of the high-quality prey enables a high conversion efficiency and control of the low-quality prey by the predator and allows for re-allocation of nutrients to the high-quality competitor. Our results show that high food quality is not necessarily detrimental for a prey species but instead can protect against extinction and promote species richness and functional biodiversity.
Chemostat experiments are employed to study predator-prey and other trophic interactions, frequently using phytoplankton-zooplankton systems. These experiments often use population dynamics as fingerprints of ecological and evolutionary processes, assuming that the contributions of all major actors to these dynamics are known. However, bacteria are often neglected although they are frequently present. We argue that even without external carbon input bacteria may affect the experimental outcomes depending on experimental conditions and the physiological traits of bacteria, phytoplankton, and zooplankton. Using a static carbon flux model and a dynamic simulation model, we predict the minimum and maximum impact of bacteria on phytoplankton-zooplankton population dynamics. Under bacteria-suppressing conditions, we find that the effect of bacteria is indeed negligible and their omission justified. Under bacteria-favoring conditions, however, bacteria may strongly affect average biomasses of phytoplankton and zooplankton. The population dynamics may become highly complex, which may result in wrong interpretations when inferring processes (e.g., trait changes) from population dynamic patterns without considering bacteria. We provide suggestions to reduce the bacterial impact experimentally. Besides optimizing experimental conditions (e.g., the dilution rate) the appropriate choice of the zooplankton predator is decisive. Counterintuitively, bacteria have a larger impact if the predator is not bacterivorous as high bacterial biomasses and complex population dynamics arise via competition for nutrients with the phytoplankton. Only at least partial bacterivory minimizes the impact of bacteria. Our results help to improve the design of chemostat experiments and their interpretation, and advance the study of ecological and evolutionary processes in aquatic food webs.
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.