Modelling and simulation play an important role in the effort of structural and functional exploration of ecological systems. Environmental risk analysis uses different types of models for exposure and effect assessment as well as for risk characterization. The integration of empirical observations and theoretical models allows the development and testing of multifactorial hypotheses on the effects of disturbance. Theory-based simulation models allow mapping and prediction of concentration-dependent effects and their extent at the population and ecosystem level (prospective modelling). Individual-based models simulate life-cycles of individual organisms and allow the extrapolation of effects on populations based on concentration-dependent life data. At the ecosystem level, populations or communities are treated as compartments and their interactions are quantified in the light of variable environmental conditions. Statistical modeling approaches develop model structures by inductive means. They use observational data to deduce causal connections and the general nature of the processes in the investigated system (retrospective modelling). The interpretation of ecosystems as networks of interacting components allows the modeling of emergent effects in terms of the self-organizing potential of the systems.
Ottermanns, Richard, Dr. (Working group Computational Ecology, UBC)
Strauß, Tido, Dr. (gaiac)
Roß-Nickoll, Martina, Dr. (Working group Community ecology and ecotoxicology)
Daniels, Benjamin, Dr.
- Individual-based models (IBM Daphnia, IBM Chaoborus, IBM Notonecta)
- Compartment models (StoLaM, landscape model)
- Development of indicators for given environmental conditions (Bioindicators)
- Gradient analysis to find drivers for community composition (Retrospective analysis)
- Visualization of community reactions under environmental stress (Effect analysis)
- Development of classification systems for natural systems (Classifier Design)
- Elucidation of hidden and indirect effects of xenobiotics on ecological communities (Dominance effects)
- Analysis of succession of disturbed systems (Biodiversity dynamics)
- Statistical analysis to validate a priori models in mechanistic / deductive modeling (Confirmatory analysis)
- Explorative analysis to elucidate multivariate correlation patterns (Generation of hypotheses)
- Stochastic simulation and scenario analysis to predict the reaction of communities as complex dynamical systems (Prospective modelling)