The response of temperate aquatic ecosystems to global warming: novel insights from a multidisciplinary project

This article serves as an introduction to this special issue of Marine Biology, but also as a review of the key findings of the AQUASHIFT research program which is the source of the articles published in this issue. AQUASHIFT is an interdisciplinary research program targeted to analyze the response of temperate zone aquatic ecosystems (both marine and freshwater) to global warming. The main conclusions of AQUASHIFT relate to (a) shifts in geographic distribution, (b) shifts in seasonality, (c) temporal mismatch in food chains, (d) biomass responses to warming, (e) responses of body size, (f) harmful bloom intensity, (f), changes of biodiversity, and (g) the dependence of shifts to temperature changes during critical seasonal windows

Mechanistic origins of variability in phytoplankton dynamics. Part II: analysis of mesocosm blooms under climate change scenarios

Driving factors of phytoplankton spring blooms have been discussed since long, but rarely analyzed quantitatively. Here, we use a mechanistic size-based ecosystem model to reconstruct observations made during the Kiel mesocosm experiments (2005–2006). The model accurately hindcasts highly variable bloom developments including community shifts in cell size. Under low light, phytoplankton dynamics was mostly controlled by selective mesozooplankton grazing. Selective grazing also explains initial dominance of large diatoms under high light conditions. All blooms were mainly terminated by aggregation and sedimentation. Allometries in nutrient uptake capabilities led to a delayed, post-bloom dominance of small species. In general, biomass and trait dynamics revealed many mutual dependencies, while growth factors decoupled from the respective selective forces. A size shift induced by one factor often changed the growth dependency on other factors. Within climate change scenarios, these indirect effects produced large sensitivities of ecosystem fluxes to the size distribution of winter phytoplankton. These sensitivities exceeded those found for changes in vertical mixing, whereas temperature changes only had minimal impacts

Spring bloom succession, grazing impact and herbivore selectivity of ciliate communities in response to winter warming

This study aimed at simulating different degrees of winter warming and at assessing its potential effects on ciliate succession and grazing-related patterns. By using indoor mesocosms filled with unfiltered water from Kiel Bight, natural light and four different temperature regimes, phytoplankton spring blooms were induced and the thermal responses of ciliates were quantified. Two distinct ciliate assemblages, a pre-spring and a spring bloom assemblage, could be detected, while their formation was strongly temperature-dependent. Both assemblages were dominated by Strobilidiids; the pre-spring bloom phase was dominated by the small Strobilidiids Lohmaniella oviformis, and the spring bloom was mainly dominated by large Strobilidiids of the genus Strobilidium. The numerical response of ciliates to increasing food concentrations showed a strong acceleration by temperature. Grazing rates of ciliates and copepods were low during the pre-spring bloom period and high during the bloom ranging from 0.06 (Δ0°C) to 0.23 day−1 (Δ4°C) for ciliates and 0.09 (Δ0°C) to 1.62 day−1 (Δ4°C) for copepods. During the spring bloom ciliates and copepods showed a strong dietary overlap characterized by a wide food spectrum consisting mainly of Chrysochromulina sp., diatom chains and large, single-celled diatoms

Warming induces shifts in microzooplankton phenology and reduces the time-lag between phytoplankton and protozoan production.

Indoor mesocosm experiments were conducted to test for potential climate change effects on the spring succession of Baltic Sea plankton. Two different temperature (Δ0 °C and Δ6 °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 °C−1 earlier and occurred almost synchronously with biomass peaks of phytoplankton in the warm mesocosms (Δ6 °C). The peak magnitudes of microzooplankton biomass remained unaffected by temperature, and growth rates of microzooplankton were higher at Δ6 °C (μ∆0 °C = 0.12 day−1 and μ∆6 °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 Q10 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

Challenges and opportunities for integrating lake ecosystem modelling approaches

A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others (‘reinventing the wheel’). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available (‘having tunnel vision’). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: super-individual models (Piscator, Charisma), physiologically structured models, stage-structured models and trait-based models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its ‘leading principle’, there are many opportunities for combining approaches. We take the point of view that a single ‘right’ approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative view on the functioning of lake ecosystems. We end with a set of specific recommendations that may be of help in the further development of lake ecosystem model

Individual Cell Based Traits Obtained by Scanning Flow-Cytometry Show Selection by Biotic and Abiotic Environmental Factors during a Phytoplankton Spring Bloom

In ecology and evolution, the primary challenge in understanding the processes that shape biodiversity is to assess the relationship between the phenotypic traits of organisms and the environment. Here we tested for selection on physio-morphological traits measured by scanning flow-cytometry at the individual level in phytoplankton communities under a temporally changing biotic and abiotic environment. Our aim was to study how high-frequency temporal changes in the environment influence biodiversity dynamics in a natural community. We focused on a spring bloom in Lake Zurich (Switzerland), characterized by rapid changes in phytoplankton, water conditions, nutrients and grazing (mainly mediated by herbivore ciliates). We described bloom dynamics in terms of taxonomic and trait-based diversity and found that diversity dynamics of trait-based groups were more pronounced than those of identified phytoplankton taxa. We characterized the linkage between measured phytoplankton traits, abiotic environmental factors and abundance of the main grazers and observed weak but significant correlations between changing abiotic and biotic conditions and measured size-related and fluorescence-related traits. We tested for deviations in observed community-wide distributions of focal traits from random patterns and found evidence for both clustering and even spacing of traits, occurring sporadically over the time series. Patterns were consistent with environmental filtering and phenotypic divergence under herbivore pressure, respectively. Size-related traits showed significant even spacing during the peak of herbivore abundance, suggesting that morphology-related traits were under selection from grazing. Pigment distribution within cells and colonies appeared instead to be associated with acclimation to temperature and water chemistry. We found support for trade-offs among grazing resistance and environmental tolerance traits, as well as for substantial periods of dynamics in which our measured traits were not under selection.