Comparing two sets of forest cover change knowledge used in forest landscape management planning

Forecasts of future resource states are central to resource management planning. Many simulation models and planning tools are used to produce such forecasts and apply knowledge of resource change dynamics as key input. Consistency among knowledge sources is therefore important to avoid knowledge ambiguity and uncertainty in resource forecasts and management plan outcomes. Using Ontario's boreal forest landscape as a case study, this paper examined two knowledge sources of forest resource change, practitioner expertise and research studies, commonly applied in plans and policies for large forest landscapes. The two knowledge sources were quantitatively compared by constructing networks of forest cover change for both sources and determining their agreement in structure and transition times. Some networks agreed well, indicating little knowledge ambiguity and comparatively low uncertainty if they were used to forecast forest landscapes. Other networks showed low agreement, thus indicating higher knowledge ambiguity and a dilemma of choice for forest landscape planners who may have to select from these knowledge sets. It is suggested that knowledge disagreements may be widespread in knowledge-driven management planning of many natural resource types and their causes similar. These disagreements signal areas of knowledge uncertainty, where resource planners must address resulting uncertainty of management outcomes and research should focus on improving resource change knowledge.