Suchergebnisse

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

The US National Greenhouse Gas Inventory uses the component ratio method (CRM), a volume conversion approach that incorporates models for tree biomass components, for forest carbon assessments. However, the performance of the CRM relative to other methods, as well as influences on its accuracy and precision, must be evaluated. We constructed a data-driven CRM (n-CRM), used it to predict total tree and component biomass for six US tree species, and compared this approach to a reference allometric model. We also assessed the influence of size, crown dynamics, and stem growth on the performance of both methods. Results show that the n-CRM was more accurate for four species, resulting from the inclusion of more predictor variables. Both methods had high uncertainty, but the precision of n-CRM predictions was two to eight times higher for small diameter trees (< 10 cm) across all species. Accuracy and precision of the crown component models (i. e. branches and foliage) was low, though better for pines than for hardwoods. Species-level analysis suggests that poor precision is influenced by crown traits and the size distribution of fitting datasets. Our results highlight needed improvements to the n-CRM, and motivate further development of data that facilitate predictive evaluation of biomass models. ; USDA Forest Service Forest Inventory and Analysis Program, Northern Region; Minnesota Agricultural Experiment Station; Michigan AgBioResearch through the USDA National Institute of Food and Agriculture ; Data compilation for the legacy data and the independent validation datasets, as well as B. Clough's time, were funded by the USDA Forest Service Forest Inventory and Analysis Program, Northern Region. Additional funding and support was available from the Minnesota Agricultural Experiment Station. Part of D.W. MacFarlane's time was supported with funds from Michigan AgBioResearch through the USDA National Institute of Food and Agriculture. ; Public domain authored by a U.S. government employee