Integrating dynamic and statistical modelling approaches in order to improve predictions for scenarios of environmental change
This was my PhD research, supported by the University of Potsdam Graduate School on Ecological Modelling.
Summary
Species respond to environmental change by dynamically adjusting their geographical ranges. Robust predictions of these changes are prerequisites to inform dynamic and sustainable conservation strategies. Correlative species distribution models (SDMs) relate species’ occurrence records to prevailing environmental factors to describe the environmental niche. They have been widely applied in global change context as they have comparably low data requirements and allow for rapid assessments of potential future species’ distributions. However, due to their static nature, transient responses to environmental change are essentially ignored in SDMs. Furthermore, neither dispersal nor demographic processes and biotic interactions are explicitly incorporated. Therefore, it has often been suggested to link statistical and mechanistic modelling approaches in order to make more realistic predictions of species’ distributions for scenarios of environmental change (e.g. Gutt et al. 2012). Here, we studied two different ways of such linkage. (i) Mechanistic modelling can act as virtual playground for testing statistical models and allows extensive exploration of specific questions. This ‘virtual ecologist’ approach is a powerful evaluation framework for testing sampling protocols, analyses and modelling tools (Zurell et al. 2010). We employed such an approach to systematically assess the effects of transient dynamics and ecological properties and processes on the prediction accuracy of SDMs for climate change projections (Zurell et al. 2009). That way, relevant mechanisms were identified that shape the species’ response to altered environmental conditions and which should hence be considered when trying to project species’ distribution through time. (ii) We supplemented SDM projections of potential future habitat for black grouse in Switzerland with an individual-based population model (Zurell et al. 2012a). By explicitly considering complex interactions between habitat availability and demographic processes, this allows for a more direct assessment of expected population response to environmental change and associated extinction risks. However, predictions were highly variable across simulations emphasising the need for principal evaluation tools like sensitivity analysis to assess uncertainty and robustness in dynamic range predictions. Furthermore, we identified data coverage of the environmental niche as a likely cause for contrasted range predictions between SDM algorithms (Zurell et al. 2012b). SDMs may fail to make reliable predictions for truncated and edge niches, meaning that portions of the niche are not represented in the data or niche edges coincide with data limits.
Supervisors
- Boris Schröder (TU Braunschweig, Germany)
- Volker Grimm (UFZ Leipzig, Germany)