Original ArticlesImputing plant community classifications for forest inventory plots

- We classify native plant communities (NPCs) present on forest inventory plots.
- Coincidence of NPC and forest inventory observations informs classification.
- Additional vegetative and physiographic predictor inputs provide improved accuracy.
- Estimates of forested NPC extent in Minnesota are produced from imputation results.
- Implications for regional management of biodiversity are discussed.

Native plant community (NPC) classifications typically require on-site visits and in-depth observations by trained ecologists. The goal is to identify unique floristic and environmental characteristics indicative of a particular plant community, ecosystem, or demographic condition. Such data are often desired to inform management decisions on sustainable timber and ecosystem services production over local to large landscapes. Yet, the time and funding needed to identify, assess, catalogue, and map these communities is often limited. Lacking these classifications, we rely on imprecise determinations of the prevalence of various NPCs. Further, extrapolating statewide NPC extent from previously imputed classifications for state managed stands is difficult without a representative sampling design including all ownerships. As a solution to the NPC sample coverage limitation, we describe an extension of a previously reported imputation model to provide the desired statewide classifications and corresponding estimates of the ecological landscape state indicator provided by NPC extent.First, NPC observations from the Minnesota Department of Natural Resources (MNDNR) Division of Ecological and Water Resources for 1964-2015 were linked with MNDNR Forest Inventory Management (FIM) stand data to provide a set of observed polygons for training the imputation model. Then, USDA Forest Service Forest Inventory and Analysis (FIA) plot data, were associated with the observed stands to provide NPC classifications for a subset of plots (e.g., training plots) contained in the FIA database for Minnesota. NPC information was then linked to forest inventory and physiographic layers via spatial association techniques in a geographic information system. Soils data describing drainage, productivity, thickness of the rooting zone, and land position were also used. Finally, validation of resulting imputed classifications shows that application of the model to the statewide FIA inventory will result in an error rate between 8% and 30% with a mean of 83% of imputations correct at the class level.We then updated the publicly accessible FIA database for Minnesota with imputed NPC classifications and scripted labeling schemes integrated with the EVALIDator report building tool to produce estimates of forestland extent. Here, we focus on estimates of NPC class by FIA Survey Unit and inventory year. Finally, quantified estimates of landscape state (e.g., NPC extent and condition) are enabled for inventories ending between 1977 and 2014. Imputed data from this series of statewide inventories enables the analysis of landscape change, and facilitates strategic planning to move the bioregional landscape in a desired ecological direction, or to provide specific ecosystem services.