Marco A. Janssen

Maintaining safe operating spaces for exploited natural systems in the face of uncertainty is a key sustainability challenge. This challenge can be viewed as a problem in which human society must navigate in a limited space of acceptable futures in which humans enjoy sufficient well-being and avoid crossing planetary boundaries. A critical obstacle is the nature of society as a controller with endogenous dynamics affected by knowledge, values, and decision-making fallacies. We outline an approach for analyzing the role of knowledge infrastructure in maintaining safe operating spaces. Using a classic natural resource problem as an illustration, we find that a small safe operating space exists that is insensitive to the type of policy implementation, while in general, a larger safe operating space exists which is dependent on the implementation of the “right” policy. Our analysis suggests the importance of considering societal response dynamics to varying policy instruments in defining the shape of safe operating spaces.

Savanna rangelands are characterized by dynamic interactions between grass, shrubs, fire and livestock driven by highly variable rainfall. When the livestock are grazers (only or preferentially eating grass) the desirable state of the system is dominated by grass, with scattered trees and shrubs. However, the system can have multiple stable attractors and a perturbation such as a drought can cause it to move from such a desired configuration into one that is dominated by shrubs with very little grass. In this paper, using the rangelands of New South Wales in Australia as an example, we provide a methodology to find robust management strategies in the context of this complex ecological system driven by stochastic rainfall events. The control variables are sheep density and the degree of fire suppression. By comparing the optimal solution where it is assumed the manager has perfect knowledge and foresight of rainfall conditions with one where the rainfall variability is ignored, we found that rainfall variability and the related uncertainty lead to a reduction of the possible expected returns from grazing activity by 33%. Using a genetic algorithm, we develop robust management strategies for highly variable rainfall that more than doubles expected returns compared to those obtained under variable rainfall using an optimal solution based on average rainfall (i.e., where the manager ignores rainfall variability).

Our analysis suggests some key features of a robust strategy. The robust strategy is precautionary and is forced by rainfall variability. It is less reactive with respect to grazing pressure changes and more reactive with respect to fire suppression than is an optimum strategy based on a deterministic system (no rainfall variability). Finally, the costs associated with implementing a robust strategy are far less than the expected economic losses when uncertainty is not taken into account.

Keywords: Rangelands; Multiple stable states; Robust management; Genetic algorithms