Modelling and simulation

Modelling and simulation are of increasing importance to explore and understand structural and functional characteristics of ecological systems. Environmental assessment uses different types of models to investigate exposure and mechanisms and for risk characterization.

Statistical modeling approaches develop model structures by inductive means. The integration of empirical observations and theoretical models allows the development and testing of multifactorial hypotheses about effects and mechanisms of disturbance. They use observational data to deduce causal connections and the general nature of the processes in the investigated system (e.g. in retrospective modelling). The interpretation of ecosystems as networks of interacting components allows modeling of emergent effects in terms of self-organizing processes in the systems.

Theory-based simulation models allow mapping and prediction of effects and their extent at the population and ecosystem level (e.g. in prospective modelling). Individual-based models simulate life-cycles of individual organisms and allow extrapolation of effects in 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.


Group management

Ottermanns, Richard, Dr. rer. nat. Dipl.-Ing.
Strauß, Tido, Dr. (gaiac)
Roß-Nickoll, Martina, Prof. Dr. 


Statistical Modelling:

  • Stochastic simulation and scenario analysis to model the reaction of populations and communities as complex dynamical systems under environmental stress
  • Elucidation of hidden and indirect effects of xenobiotics
  • Sensitivity analysis to validate a priori models in mechanistic modeling
  • Analysis of succession and drivers for community composition in disturbed systems
  • Development of classification systems and indicators for environmental conditions

Mechanistic Simulation:

  • Individual-based models (IBM Daphnia, IBM Chaoborus, IBM Notonecta)
  • Compartment models (MITAS, StoLaM, landscape model)