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Estimation of live tree biomass is an important task for both forest carbon accounting and studies of nutrient dynamics in forest ecosystems. In this study, we took advantage of an extensive felled-tree database (with 2885 foliage biomass observations) to compare different models and grouping schemes based on phylogenetic and geographic variation for predicting foliage biomass at the tree scale. We adopted a Bayesian hierarchical statistical framework, first to compare linear models that predict foliage biomass directly to models that separately estimate a foliage ratio as a component of total aboveground biomass, then to compare species specific models to both 'narrow' and 'broad' general biomass models using the best fitted functional form. We evaluated models by simulating new datasets from the posterior predictive distribution, using both summary statistics and visual assessments of model performance. Key findings of our study were: (1) simple linear models provided a better fit to our data than component ratio models, where total biomass and the foliar ratio are estimated separately; (2) species-specific equations provided the best predictive performance, and there was no advantage to narrow species groupings relative to broader groups; and (3) all three model schemes (i.e., species-specific models versus narrow or broad groupings proposed in national-scale biomass equations) tended to over-predict foliage biomass and resulted in predictions with very high uncertainty, particularly for large diameter trees. This analysis represents a fundamental shift in carbon accounting by employing felled-tree data to refine our understanding of uncertainty associated with component biomass estimates, and presents an ideal approach to account for tree-scale allometric model error when estimating forest carbon stocks. However, our results also highlight the need for substantial improvements to both available fitting data and models for foliage biomass before this approach is implemented within the context of greenhouse gas inventories. ; U.S. Department of Agriculture, Forest Service, Northern Research StationUnited States Department of Agriculture (USDA)United States Forest Service; Minnesota Agricultural Experiment Station ; We wish to thank David Walker, Jereme Frank, Aaron Weiskittel, and all who have contributed to the U.S. Forest Service Volume Biomass Project and the Legacy database. In addition we would like to thank John Stanovick, David Bell, Kenneth Elgersma, and three anonymous reviewers for their comments on our manuscript. This research is funded by the U.S. Department of Agriculture, Forest Service, Northern Research Station and the Minnesota Agricultural Experiment Station. ; Public domain authored by a U.S. government employee

Accurate estimation of forest biomass and carbon stocks at regional to national scales is a key requirement in determining terrestrial carbon sources and sinks on United States (US) forest lands. To that end, comprehensive assessment and testing of alternative volume and biomass models were conducted for individual tree models employed in the component ratio method (CRM) currently used in the US' National Greenhouse Gas Inventory. The CRM applies species-specific stem volume equations along with specific gravity conversions and component expansion factors to ensure consistency between predicted stem volumes and weights, and additivity of predicted live tree component weights to match aboveground biomass (AGB). Data from over 76 600 stem volumes and 6600 AGB observations were compiled from individual studies conducted in the past 115 years - what we refer to as legacy data - to perform the assessment. Scenarios formulated to incrementally replace constituent equations in the CRM with models fitted to legacy data were tested using cross-validation methods, and estimates of AGB were scaled using forest inventory data to compare across 33 states in the eastern US. Modifications all indicated that the CRM in its present formulation underestimates AGB in eastern forests, with the range of underestimation ranging from 6.2 to 17 per cent. Cross-validation results indicated the greatest reductions in estimation bias and root-mean squared error could be achieved by scenarios that replaced stem volume, sapling AGB, and component ratio equations in the CRM. A change in the definitions used in apportioning biomass to aboveground components was also shown to increase prediction accuracy. Adopting modifications tested here would increase AGB estimates for the eastern US by 15 per cent, accounting for 1.5 Pg of C currently unaccounted for in live tree aboveground forest C stock assessments. Expansion of the legacy data set currently underway should be useful for further testing, such as whether similar gains in accuracy can be achieved in estimates of regional or national-scale C sequestration rates. ; U.S. Department of Agriculture Forest Service, Forest Inventory and Analysis National Program; U.S. Department of Agriculture National Institute of Food and Agriculture, McIntire-Stennis Cooperative Forestry Program ; The U.S. Department of Agriculture Forest Service, Forest Inventory and Analysis National Program, and the U.S. Department of Agriculture National Institute of Food and Agriculture, McIntire-Stennis Cooperative Forestry Program. ; Public domain authored by a U.S. government employee