Department Systems Analysis, Integrated Assessment and Modelling

Effluents of wastewater treatment plants can impact microbial communities in the receiving streams. However, little is known about the role of microorganisms in wastewater as opposed to other wastewater constituents, such as nutrients and micropollutants. We aimed therefore at determining the impact of wastewater microorganisms on the microbial diversity and function of periphyton, key microbial communities in streams. We used a flow-through channel system to grow periphyton upon exposure to a mixture of stream water and unfiltered or ultra-filtered wastewater. Impacts were assessed on periphyton biomass, activities and tolerance to micropollutants, as well as on microbial diversity. Our results showed that wastewater microorganisms colonized periphyton and modified its community composition, resulting for instance in an increased abundance of Chloroflexi and a decreased abundance of diatoms and green algae. This led to shifts towards heterotrophy, as suggested by the changes in nutrient stoichiometry and the increased mineralization potential of carbon substrates. An increased tolerance towards micropollutants was only found for periphyton exposed to unfiltered wastewater but not to ultra-filtered wastewater, suggesting that wastewater microorganisms were responsible for this increased tolerance. Overall, our results highlight the need to consider the role of wastewater microorganisms when studying potential impacts of wastewater on the receiving water body.

Aim: Biological invasions are a major threat to biodiversity in aquatic and terrestrial habitats. Insects represent an important group of species in freshwater and terrestrial habitats, and they constitute a large proportion of non-native species. However, while many non-native insects are known from terrestrial ecosystems, they appear to be less represented in freshwater habitats. Comparisons between freshwater and terrestrial habitats of invader richness relative to native species richness are scarce, which hinders syntheses of invasion processes. Here, we used data from three regions on different continents to determine whether non-native insects are indeed under-represented in freshwater compared with terrestrial assemblages.
Location: Europe, North America, New Zealand.
Methods: We compiled a comprehensive inventory of native and non-native insect species established in freshwater and terrestrial habitats of the three study regions. We then contrasted the richness of non-native and native species among freshwater and terrestrial insects for all insect orders in each region. Using binomial regression, we analysed the proportions of non-native species in freshwater and terrestrial habitats. Marine insect species were excluded from our analysis, and insects in low-salinity brackish water were considered as freshwater insects.
Results: In most insect orders living in freshwater, non-native species were under-represented, while they were over-represented in a number of terrestrial orders. This pattern occurred in purely aquatic orders and in orders with both freshwater and terrestrial species. Overall, the proportion of non-native species was significantly lower in freshwater than in terrestrial species.
Main conclusions: Despite the numerical and ecological importance of insects among all non-native species, non-native insect species are surprisingly rare in freshwater habitats. This is consistent across the three investigated regions. We review hypotheses concerning species traits and invasion pathways that are most likely to explain these patterns. Our findings contribute to a growing appreciation of drivers and impacts of biological invasions.

Increased attention directed at frozen soil hydrology has been prompted by climate change. In spite of an increasing number of field measurements and modeling studies, the impact of frozen soil on hydrological processes at the catchment scale is still unclear. However, frozen soil hydrology models have mostly been developed based on a bottom-up approach, i.e., by aggregating prior knowledge at the pixel scale, which is an approach notoriously suffering from equifinality and data scarcity. Therefore, in this study, we explore the impact of frozen soil at the catchment scale, following a top-down approach, implying the following sequence: expert-driven data analysis → qualitative perceptual model → quantitative conceptual model → testing of model realism. The complex mountainous Hulu catchment, northeast of the Qinghai-Tibet Plateau (QTP), was selected as the study site. First, we diagnosed the impact of frozen soil on catchment hydrology, based on multi-source field observations, model discrepancy, and our expert knowledge. The following two new typical hydrograph properties were identified: The low runoff in the early thawing season (LRET) and the discontinuous baseflow recession (DBR). Second, we developed a perceptual frozen soil hydrological model to explain the LRET and DBR properties. Third, based on the perceptual model and a landscape-based modeling framework (FLEX-Topo), a semi-distributed conceptual frozen soil hydrological model (FLEX-Topo-FS) was developed. The results demonstrate that the FLEX-Topo-FS model can represent the effect of soil freeze-Thaw processes on hydrologic connectivity and groundwater discharge and significantly improve hydrograph simulation, including the LRET and DBR events. Furthermore, its realism was confirmed by alternative multi-source and multi-scale observations, particularly the freezing and thawing front in the soil, the lower limit of permafrost, and the trends in groundwater level variation. To the best of our knowledge, this study is the first report of LRET and DBR processes in a mountainous frozen soil catchment. The FLEX-Topo-FS model is a novel conceptual frozen soil hydrological model which represents these complex processes and has the potential for wider use in the vast QTP and other cold mountainous regions.

For stochastic models with intractable likelihood functions, approximate Bayesian computation offers a way of approximating the true posterior through repeated comparisons of observations with simulated model outputs in terms of a small set of summary statistics. These statistics need to retain the information that is relevant for constraining the parameters but cancel out the noise. They can thus be seen as thermodynamic state variables, for general stochastic models. For many scientific applications, we need strictly more summary statistics than model parameters to reach a satisfactory approximation of the posterior. Therefore, we propose to use the inner dimension of deep neural network based Autoencoders as summary statistics. To create an incentive for the encoder to encode all the parameter-related information but not the noise, we give the decoder access to explicit or implicit information on the noise that has been used to generate the training data. We validate the approach empirically on two types of stochastic models.

In simplified models of glasses we clarify the existence of two different kinds of coexisting activated dynamics, with one of the two dominating over the other. One is the energy barrier hopping that is typically used to understand activation, and the other, which we call entropic activation, is driven by the scarcity of convenient directions in phase space. When entropic activation dominates, the height of the energy barriers is no longer the primary factor governing the system's slowdown. In our analysis, dominance of one mechanism over the other depends on temperature and the shape of the density of states. We also find that at low temperatures a phase transition between the two kinds of activation can occur. Our observations are used to provide a scenario that can harmonize the facilitation and thermodynamic pictures of the slowdown of glasses into a single description.