Research: Modelling and Simulation
UBC - Chair of Environmental Biology and Chemodynamics
ESA - Department of Ecosystem Analysis
Modelling and simulation play an important role in the study of ecological systems. Mechanistic/deterministic models are used in environmental risk assessment to perform estimation of exposition and effects as well as risk characterization itself. Simulation models are capable to reproduce and predict concentration effects on populations and ecosystems.
Individual based simulation uses life-cycle models of individuals to extrapolate concentration dependend shifts in life data to populations and multi-species systems. Individual based simulation is based on a probabilistic structure and models natural variability explicitly, which causes differences in individuals. On the ecosystem level, populations or communities are treated as compartments, quantifying the interactions (compartment models).
Empirical/inductive modelling approaches develop the model structure in an inductive way. They are based on actual data, to deduce the interactions in the system on investigation. In this approach, the inductive step plays an important role as a statistical syllogism. As this aspect is of great importance, the approach is often called statistical or stochastic modelling. Besides these mathematical methods, approaches from pattern recognition sciences are applied.
- Individual-based models (IBM Daphnia, IBM Chaoborus, IBM Notonecta)
- Compartment models (StoLaM, Landscape model)
- Research into spatio-temporal patterns, synecology and bioindicator potential of invertebrate species (bioindicator development)
- Investigations in species-species and physico-species interactions (gradient analysis, retrospective modelling)
- Visualisation of communities shifts under stress (effect analysis)
- Development of classification systems for natural communities (classifier design)
- Investigation in hidden and indirect effects of xenobiotica on communities and populations (dominance effects)
- Analysis of succession of disturbed and renatured systems (biodiversity dynamics)
- Statistical verification of a priori models in mechanistic/deductive modelling (confirmatory analysis)
- Explorative analyses to model multivariate correlation patterns (hypotheses generation)
- Stochastic simulation and scenario analyses to predict the dynamics of natural communities as complex adaptive systems (prospective modelling)