We view an Intelligent Information System (IIS) as composed of a unified knowledge base, database, and model base. This allows an IIS to provide responses to user queries regardless of whether the query process involves a data retrieval, an inference, a computational method, a problem solving module, or some combination of these. The unified integration of these components in a distributed environment for forest ecosystem management is the focus of our continuing research.

Forest ecosystems exhibit complex dynamics over time and space. Management of forest ecosystems involves the need to forecast future states of complex systems that are often undergoing structural changes. This in turn requires integration of quantitative science and engineering components with socio-political, regulatory, and economic considerations. The amount of data, information and knowledge involved in the management process is often overwhelming. Integrated decision support systems may help managers make consistently good decisions concerning forest ecosystem management. Integrating computer systems using a system-specific or custom approach has many disadvantages. We compare a variety of current approaches, suggest characteristics that an approach should have, and propose that the Distributed Component Object Model is an approach that is very suitable for forest ecosystem decision support system integration.

The gypsy moth is one of North America's most devastating exotic forest pests because it can cause the loss of valuable oak species, degraded aesthetics, loss of wildlife habitat, and detrimental effects on watersheds. Due to the increasingly wide infestation of the gypsy moth, it is important to develop decision aids that help assess the risks of this pest to our forests. Expert systems are a type of decision aid that could be applied to the area of risk assessment. We have developed the Gypsy Moth Expert System to estimate the risk that a forest stand faces from the gypsy moth based on the composition, structure, and management objectives of a particular forest. Risk assessment in this context is developed from forest susceptibility to infestation, vulnerability to damage caused by an infestation, and the hazard that management objectives for a forest may be affected if damage occurs. The system uses a straightforward set of if-then rules to classify risk. The development of a web-based expert system presented significant challenges to maintaining remote user processing integrity.