Increasing the utility of tree regeneration inventories: Linking seedling abundance to sapling recruitment

Tree regeneration surveys provide insight into potential forest change and trajectories of stand development, which can guide management in an era of global change. However, most tree regeneration surveys tend to be coarse and/or rapid assessments which can introduce considerable uncertainty into translating estimates of seedling abundance into models of sapling recruitment and subsequent overstory tree abundance and composition. Observations of seedling abundance changes across size classes may be essential to accurately predicting recruitment from seedling sized trees to advanced size classes, which is fundamental to informing our understanding of future forest composition and dynamics. Using the USDA Forest Service's Forest Inventory and Analysis (FIA) program's Regeneration Indicator (RI) dataset, in which seedlings are monitored by six height classes, we developed Boosted Regression Tree models to predict presence of sapling recruitment for five common, north temperate and boreal tree species as a function of seedling abundance by height class and site/stand factors. Models using the six RI seedling height classes were compared to models using the single seedling size class as commonly surveyed by programs such as FIA. Use of seedling height classes improved models for all species. Seedlings > 1.5 m tall were the most influential predictors of recruitment for each species while seedlings in classes < 1.5 m tall were either removed entirely from models or had low relative influence (<8%). Seedlings < 0.3 m tall had both positive and negative relationships with sapling recruitment depending on species, suggesting that abundances of small seedlings should be interpreted cautiously. This approach demonstrates the importance of collecting relatively coarse seedling height data during regeneration surveys with potential application to other regions and scenarios to expand the utility of tree regeneration surveys to predict future forest dynamics.