Invasive alien plants and water resources in South Africa : current understanding, predictive ability and research challenges
CITATION: Gorgens, A. H. M. & Van Wilgen, B. W. 2004. Invasive alien plants and water resources in South Africa : current understanding, predictive ability and research challenges. South African Journal of Science, 100(1-2):27-33.
The original publication is available at https://journals.co.za
Predictions that invasive alien plants would use significant amounts of water were a major factor in the establishment of South Africa's Working for Water programme, which aims to protect water resources by clearing these plants. The predictions were made by combining the results of hydrological experiments, conducted to assess the effects of afforestation with alien trees on water resources, with an ecological understanding of the spread and establishment of invasive trees. The forecasts were then scaled up to arrive at national estimates of the corresponding water consumption. This paper reviews the approaches that have been used to estimate these consequences at different scales. We propose a framework for assessing the process of knowledge generation, and review the approaches used in South Africa at each level of the framework, the current level of understanding arising from the use of these methods, and significant gaps in understanding. The framework has four levels: fundamental observations from which a detailed understanding of biophysical processes can be developed; applied or predictive research from which an understanding of processes can be scaled up to predict generic outcomes; integrative research where a predictive understanding of hydrology can be combined with information from other disciplines to place the outcomes in a wider context; and research on management support, such that the information can be used to improve management and policy decisions. We conclude that much knowledge exists, but that there are also significant gaps in understanding, and challenges associated with scaling up and down to make appropriate predictions. This is especially true at the management support level, where very different kinds of uncertainties operate in the same comparative framework. Existing knowledge needs also to be used more effectively, to help prioritize clearing operations by targeting areas in terms of water-related benefits.