Ecological modelling has traditionally studied the temporal dynamics of systems. Classical examples are population dynamics, predator prey oscillations, succession, climate change processes, or the concepts of resistance, recovery and resilience. However, a lot has changed since Peter Kareiva (1994) proclaimed ‘Space’ to be the ‘final frontier for ecological theory’. The interest in spatial relations and patterns has become more prevalent, and today most ecological models are spatially explicit. A major driving factor behind this trend has been a strive towards more realistic models, together with a rapidly growing availability and accessibility of spatial data and the necessary computer power to process these data.
In my talk, I will use ongoing projects in my research group on Spatial Simulation at the Department of Geoinformatics – Z_GIS to highlight the added value that spatially-explicit modelling can hold for advancing the understanding of complex ecological systems: What is special about spatial? How can the concept of individual-based ecology and associated geocomputational methods help to unveil how a system functions? Can the spatial perspective help ecological models to learn from ‘big data’ and turn it into ‘big understanding’? Last but not least, I will discuss what are the challenges ahead, where are the limitations and what are the most promising lines of further research.