Site-specific versus pantropical allometric equations: Which option to estimate the biomass of a moist central African forest?

• Existing pantropical biomass allometric equations do not rely on African data.
• The biomass of 101 trees was destructively measured in northeastern Gabon.
• New site-specific allometric equations were fitted to these data.
• Chave et al. (2005) equations for moist forest overestimated the biomass by 40%.
• More data on tree biomass are needed in central African moist forests.

Fewer data on tree biomass and allometric equations are available in African tropical moist forests than in the other tropical continents. When needing a biomass allometric equation, one thus faces the dilemma of using either a pantropical equation with the risk that it is biased for Africa, or a site-specific equation that is imprecise. Using a data set on aboveground biomass for 101 trees destructively measured at Zadié in northeastern Gabon, we fitted site-specific allometric equations and assessed the validity of ten existing equations. The best fitted model without height as a predictor was: B=exp[-4.0596+4.0624lnD-0.228(lnD)2+1.4307lnρ]B=exp[-4.0596+4.0624lnD-0.228(lnD)2+1.4307lnρ], whereas the best fitted model with height was: B=exp[-2.5680+0.9517ln(D2H)+1.1891lnρ]B=exp[-2.5680+0.9517ln(D2H)+1.1891lnρ], where B is the aboveground biomass in kg, D the diameter at breast height in cm, H the height in m, and ρ the wood density in g cm−3. Separate allometric equations for the stem, stump, foliage and branches were also fitted. Chave et al. (2005)’s pantropical equations for moist forests, that are currently the most commonly used allometric equations in central Africa, were not valid at Zadié with an overestimation of biomass of about 40%. The allometric equations of the same authors for wet forests were valid at Zadié, even though the climatic zone does not correspond. More data on tree biomass are needed in central Africa to explore the natural range of variability in tree biomass and identify the factors that influence variations among sites.