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Photo of Dr Bert Wuyts

Dr Bert Wuyts

Postdoctoral Research Fellow


Telephone: 01392 723590

Extension: (Streatham) 3590

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I am a Postdoctoral Research Fellow specialising in analysis and modelling of real-world ecosystems via complex systems approaches. My BSc/MSc were an interdisciplinary mix of environmental sciences and physics (physical geograpy and physics at KULeuven, Belgium; environmental systems science at ETH Zuerich, Switzerland). I obtained my PhD at the University of Bristol (Centre for Complexity Sciences), focusing on alternative stable vegetation states in Amazonia and Africa (with Prof. Alan Champneys and Dr. Joanna House). 

Research interests

The motto of the complexity sciences is that complicated dynamics can emerge from simple rules. Yet, for the study of real ecosystems, idealised models of complex systems do not suffice. In my work, I use models of idealised complex systems and relax their idealisations. The assumptions I have so far been working on have to do with spatial homogeneity and the nature of spatial interaction. The application I have mostly worked on is catastrophic transitions between rainforest and tropical savanna in the Amazon rainforest.

In my PhD, I used ad-hoc partial differential equation descriptions of a locally bistable model of the Amazon rainforest and showed that relaxing the assumption of spatial homogeneity can lead to complete elimination of bistability, which was confirmed by our analysis of geospatial data of the Amazon region. The PDE description relies implicitly on a mean-field assumption (corresponding to the Ginzburg-Landau phenomenology in the physics of phase transitions). In my current work (with Prof. Jan Sieber), I am testing this assumption by deriving and analysing coarse-grained descriptions of micro-scale stochastic spatial models of vegetation dynamics, via approximations of the master equation. I use the tools of dynamical systems and bifurcation theory to study the stability of the resulting differential equations, but I also rely on equation-free methods applied directly to the stochastic model.