Structure and causes of vegetation change in state and transition model applications.

Abstract

State and transition (ST) descriptions of rangeland vegetation dynamics provide information on current perceptions of explicit causes of change in dominant vegetation. Structural attributes of ST applications allow an evaluation of the complexity of the ST model and comparisons with the organization of the traditional succession-retrogression model of secondary succession. An analysis of 29 applications of the ST model revealed consistent trends. The number of transitions connecting states showed a less-than-expected increase with the size of the application. This is probably associated with limitations to interpret complex relationships and a need to produce relatively simple applications. Larger applications exhibited a shift towards stable states with pivotal positions within structures less connected (ie., with fewer transitions) than expected by chance for a given number of states. Thus, some stable states assume key intermediary roles as the number of states considered increases. It is debatable whether this is a property of larger systems or an effect of modeling bias. The analysis of causes of vegetation change confirmed current perceptions about the importance of man-related sources of disturbance. Grazing, fire, and control of woody plant species are visualized as the most relevant man-related agents of change. Some ST applications retain autogenic behaviors embedded in transitions in spite of the event-driven nature of the approach. However, the ST model removes autogenic processes from their central role as general causes for vegetation change. This approach is theoretically very limited because no general properties or attributes of the components (e.g., plant species assemblages, individual species) or processes (e.g., growth, reproduction, mineralization) of the system are used in any comprehensive way to generate predictive rules of wider than local relevance. Alternative approaches are suggested that would allow ecological generalizations and comparisons across systems.