Department Systems Analysis, Integrated Assessment and Modelling

Project Overview

Bridging the gap between data science and mechanistic modelling for a better understanding of community composition.
Deep Neural Networks (DNNs) have shown empirical performance but they are still nevertheless a black-box function modeling data
We test a big data workflow for understanding and predicting plankton dynamics using monitoring data.
Mechanistic modelling of the macroinvertebrate community composition in rivers.
We use machine learning methods to predict the effects of chemicals on aquatic species.
Activated dynamics is a very slow process that takes place on exponentially large time scales. Usually it is associated to barrier hopping.
We study recent developments in insect abundance, biomass and species richness in terrestrial and aquatic ecosystems of Switzerland.
Considering uncertainty and ambiguity in societal preferences
Investigating model properties beyond the quality of fit
Exploring the use of machine learning techniques to uncover low-dimensional features within high-dimensional datasets, both simulated and observed
Studying interactions between terrestrial avian predators and aquatic fish prey at different spatial scales and their impact on movement, behavior and population dynamics
Underwater acoustic receivers are widely deployed to study animal movement. We develop state-of-the-art modelling methods for this research.
A core ecological hypothesis is that species are more abundant near range centres. We are testing this hypothesis across groundfish in the northern hemisphere.
We characterize the bi-directional relationships between water resources, refugee migrations and armed conflicts.
We seek to determine why so few international treaties exist over shared aquifers
Framework to operationalize the human right to water in water extractive industries

Completed Projects

We compare invasions in aquatic and terrestrial ecosystems primarily at large (national) spatial scales and among several higher-level taxa (insects, molluscs, crustaceans, all major vertebrate classes, and plants).
Heterogeneous data platform for operational modeling and forecasting of Swiss lakes in collaboration with the Swiss Data Science Center.
Decision support for river management by combining the prediction of effects of suggested measures with quantified societal goals.
We investigate human impacts on the community composition of macroinvertebrates and fish in Swiss rivers with statistical analyses of existing monitoring data, food web analyses, and computer models.
Understanding trout meta-populations in river networks and predicting the effects of habitat restoration measures.
Complex systems theory meets big phytoplankton trait data.
Development of unified ecological assessment procedures for river management.
Restoring rivers for for effective catchment management.
Hypothesis testing using controlled experiments to characterize diffuse pollution in small agricultural catchments
Towards a better understanding and more reliable predictions of complex systems dynamics.
Development of a dynamical model to simulate the water and water related energy flows in function of time.
Community detection consists of extracting the affinity between agents of a system, which is extracted from quantities such as the frequency of interactions.