Error propagation and scaling for tropical forest biomass estimates

The dry above-ground biomass (AGB) of tropical forests is a crucial variable for ecologists, biogeochemists, foresters, and policy makers. Permanent tree inventories provide an efficient way of assessing this variable. In order to make correct inferences about long-term changes in biomass stocks, it is essential to know the uncertainty associated with AGB estimates, yet this uncertainty is seldom evaluated carefully. Here, four types of uncertainties that could lead to statistical error in AGB estimates are quantified: error due to tree measurement; error due to the choice of allometric model relating AGB to other tree dimensions; sampling uncertainty, related to the size of the study plot; representativeness of a network of small plots across a forest landscape. All four are estimated for a 50-hectare plot on Barro Colorado Island, and for a network of 1-hectare plots scattered across the Panama Canal Watershed, Central Panama. This chapter finds that the most important source of error is currently related to the choice of the allometric model. More work should be devoted to improving the predictive power of allometric models for biomass.