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Comparing tree foliage biomass models fitted to a multispecies, felled-tree biomass dataset for the United States

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  • Clough, Brian J.
  • Russell, Matthew B.
  • Domke, Grant M.
  • Woodall, Christopher W.
  • Radtke, Philip J.

Abstract

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.

Suggested Citation

  • Clough, Brian J. & Russell, Matthew B. & Domke, Grant M. & Woodall, Christopher W. & Radtke, Philip J., 2016. "Comparing tree foliage biomass models fitted to a multispecies, felled-tree biomass dataset for the United States," Ecological Modelling, Elsevier, vol. 333(C), pages 79-91.
    • Handle: RePEc:eee:ecomod:v:333:y:2016:i:c:p:79-91
    DOI: 10.1016/j.ecolmodel.2016.04.009
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    References listed on IDEAS

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    1. A. Gelman & Y. Goegebeur & F. Tuerlinckx & I. Van Mechelen, 2000. "Diagnostic checks for discrete data regression models using posterior predictive simulations," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(2), pages 247-268.
    2. Brian J. Enquist & Karl J. Niklas, 2001. "Invariant scaling relations across tree-dominated communities," Nature, Nature, vol. 410(6829), pages 655-660, April.
    3. Andrew Gelman, 2003. "A Bayesian Formulation of Exploratory Data Analysis and Goodness‐of‐fit Testing," International Statistical Review, International Statistical Institute, vol. 71(2), pages 369-382, August.
    4. Picard, Nicolas & Mortier, Frédéric & Rossi, Vivien & Gourlet-Fleury, Sylvie, 2010. "Clustering species using a model of population dynamics and aggregation theory," Ecological Modelling, Elsevier, vol. 221(2), pages 152-160.
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