Robust strategies for managing rangelands with multiple stable attractors

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. r 2003 Elsevier Science (USA). All rights reserved.