How can social-ecological system models simulate the emergence of social-ecological crises?
The idea that human impacts on natural systems might trigger large-scale, social-ecological 'crises' or 'breakdowns' is attracting increasing scientific, societal and political attention, but the risks of such crises remain hard to assess or ameliorate. Social-ecological systems have complex dynamics, with bifurcations, nonlinearities and tipping points all emerging from the interaction of multiple human and natural processes. Computational modelling is a key tool in understanding these processes and their effects on system resilience. However, models that operate over large geographical extents often rely on assumptions such as economic equilibrium and optimisation in social-economic systems, and mean-field or trend-based behaviour in ecological systems, which limit the simulation of crisis dynamics. Alternative forms of modelling focus on simulating local-scale processes that underpin the dynamics of social-ecological systems. Recent improvements in data resources and computational tools mean that such modelling is now technically feasible across large geographical extents. We consider the contributions that the different types of model can make to simulating social-ecological crises. While no models are able to predict exact outcomes in complex social-ecological systems, we suggest that one new approach with substantial promise is hybrid modelling that uses existing model architectures to isolate and understand key processes, revealing risks and associated uncertainties of crises emerging. We outline convergent and efficient functional descriptions of social and ecological systems that can be used to develop such models, data resources that can support them, and possible 'high-level' processes that they can represent.