How to improve allometric equations to estimate forest biomass stocks? Some hints from a central African forest

Predicting the biomass of a forest stand using forest inventory data and allometric equations involves a chain of propagation of errors going from the sampling error to the tree measurement error. Using a biomass data set of 101 trees in a tropical rain forest in Gabon, we compared two sources of error: the error due to the choice of allometric equation, assessed using Bayesian model averaging, and the biomass measurement error when tree biomass is calculated from tree volume rather than directly weighed. Differences between allometric equations resulted in a between-equation error of about 0.245 for log-transformed biomass compared with a residual within-equation error of 0.297. Because the residual error is leveled off when randomly accumulating trees whereas the between-equation error is incompressible, the latter turned out to be a major source of error at the scale o fa1h aplot. Measuring volumes rather than masses resulted in an error of 0.241 for log-transformed biomass and an average overestimation of the biomass by 19%. These results confirmed the choice of the allometric equation as a major source of error but unexpectedly showed that measuring volumes could seriously bias biomass estimates.