Error propagation and scaling for tropical forest biomass estimates

TLDR

Above‑ground biomass of tropical forests is a key variable for assessing carbon stocks, yet uncertainty in its estimates is rarely quantified. The study aims to quantify four sources of uncertainty in AGB estimates: measurement error, allometric model choice, sampling size, and plot representativeness. The authors estimate these uncertainties using data from a 50‑ha plot on Barro Colorado Island and a network of 1‑ha plots across central Panama. The largest source of error is the choice of allometric model, indicating a need to improve its predictive power.

Abstract

The above-ground biomass (AGB) of tropical forests is a crucial variable for ecologists, biogeochemists, foresters and policymakers. Tree inventories are an efficient way of assessing forest carbon stocks and emissions to the atmosphere during deforestation. 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 rarely evaluated carefully. Here, we quantify four types of uncertainty that could lead to statistical error in AGB estimates: (i) error due to tree measurement; (ii) error due to the choice of an allometric model relating AGB to other tree dimensions; (iii) sampling uncertainty, related to the size of the study plot; (iv) representativeness of a network of small plots across a vast forest landscape. In previous studies, these sources of error were reported but rarely integrated into a consistent framework. We estimate all four terms in a 50 hectare (ha, where 1 ha = 10<sup>4</sup> m<sup>2</sup>) plot on Barro Colorado Island, Panama, and in a network of 1 ha plots scattered across central Panama. We find 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.