There are large investments in native vegetation restoration being carried out worldwide, but there is high uncertainty about how, where, and under what circumstances vegetation restoration is a good investment. This is a common feature of natural resource management, because biological systems are highly complex, and actions may be carried out over broad temporal and spatial scales. The issue is of great importance to governments at all levels as vegetation restoration is likely to be a popular climate adaptation investment option. Adaptive Management (AM) is commonly advocated as a way to deal with this issue. However, despite a long rhetorical commitment to AM, there still remains a need to demonstrate its efficacy. One of the major factors impeding implementation is the failure to develop and use appropriate process models, which are a core element of AM. Process models are a representation of the belief about the properties and dynamics of an ecological system, and the system response to management intervention. In this study we present the first example of AM underpinned by a vegetation condition state and transition model (STM) for native woodland vegetation dynamics. We use Bayesian Belief Networks to structure the process model.