Considering ecological information in forest management planning has long been of interest to applied forest ecologists and managers. While managed forest stands provide ecological benefits, these may be different from the services and values supported by native ecosystems and plant communities. To better understand the implications of management for biological diversity, ecosystem services, timber and other interests, an ecological classification methodology matched with existing forest inventory and management operations is proposed. This methodology makes use of nearly 17,000 native plant community (NPC) observations provided by the Minnesota Department of Natural Resources (MNDNR) and others. These observations cover the period from 1964 – 2015, and coincide with stands monitored by the MNDNR Division of Forestry. Here, we extrapolate observed ecological conditions and growth information to very similar, nearby management units using a categorical maximum likelihood approach to imputation. We repeat the procedure for systematic forest inventory plots overlaid with NPC classified stands. The resulting representative sample of observed entities is then used to impute classifications for the full set of inventory records. This process allows for quantification of the ecological landscape state indicators (e.g., in terms of area occupied and other characteristics) provided by NPC classifications and associated data.
With a success rate of 83% for imputation of NPC class on USDA Forest Service Forest Inventory and Analysis (FIA) plots sampled between 1977 and 2014, the process described here efficiently classifies forest inventory units with respect to native plant community association. The outcome of this approach could have broad implications for extending our understanding of and capacity to sustainably manage broadly distributed regional forest landscapes. Analysis of observed and imputed classifications also indicates that NPC class often provides insight on different growth patterns and eventual yield of forested stands. This enhanced understanding of landscape ecological conditions can, in turn, lead to better informed management decisions. While this work addresses one step in the iterative process of adaptive landscape management, it bridges the gap between our understanding of landscape ecological characteristics and using that information to set specific, actionable management goals. Given a set of landscape ecological state indicators, like that provided by NPC class, we can begin to answer questions about the adequacy of current conditions for habitat, biodiversity, demographic sustainability, and societal values derived from the forested landscape. We can then set rational and achievable management goals connected to strategic actions supporting a desired future condition.