Relative influences of multiple sources of uncertainty on cumulative and incremental tree-ring-derived aboveground biomass estimates

Abstract

How forest growth responds to climate change will impact the global carbon cycle. The sensitivity of tree growth and thus forest productivity to climate can be inferred from tree-ring increments, but individual tree responses may differ from the overall forest response. Tree-ring data have also been used to estimate interannual variability in aboveground biomass, but a shortage of robust uncertainty estimates often limits comparisons with other measurements of the carbon cycle across variable ecological settings. Here we identify and quantify four important sources of uncertainty that affect tree-ring-based aboveground biomass estimates: subsampling, allometry, forest density (sampling), and mortality. In addition, we investigate whether transforming rings widths into biomass affects the underlying growth-climate relationships at two coniferous forests located in the Valles Caldera in northern New Mexico. Allometric and mortality sources of uncertainty contributed most (34–57 and 24–42%, respectively) and subsampling uncertainty least (7–8%) to the total uncertainty for cumulative biomass estimates. Subsampling uncertainty, however, was the largest source of uncertainty for year-to-year variations in biomass estimates, and its large contribution indicates that between-tree growth variability remains influential to changes in year-to-year biomass estimates for a stand. The effect of the large contribution of the subsampling uncertainty is reflected by the different climate responses of large and small trees. Yet, the average influence of climate on tree growth persisted through the biomass transformation, and the biomass growth-climate relationship is comparable to that found in traditional climate reconstruction-oriented tree-ring chronologies. Including the uncertainties in estimates of aboveground biomass will aid comparisons of biomass increment across disparate forests, as well as further the use of these data in vegetation modeling frameworks.