Tree allometry and improved estimation of carbon stocks and balance in tropical forests

TLDR: A critical reassessment of the quality and the robustness of these models across tropical forest types, using a large dataset of 2,410 trees ≥ 5 cm diameter, directly harvested in 27 study sites across the tropics, is provided.

Abstract: Tropical forests hold large stores of carbon, yet uncertainty remains regarding their quantitative contri- bution to the global carbon cycle. One approach to quantifying carbon biomass stores consists in inferring changes from long-term forest inventory plots. Regres- sion models are used to convert inventory data into an estimate of aboveground biomass (AGB). We provide a critical reassessment of the quality and the robustness of these models across tropical forest types, using a large dataset of 2,410 trees ‡ 5 cm diameter, directly harvested in 27 study sites across the tropics. Proportional rela- tionships between aboveground biomass and the prod- uct of wood density, trunk cross-sectional area, and total height are constructed. We also develop a regres- sion model involving wood density and stem diameter only. Our models were tested for secondary and old- growth forests, for dry, moist and wet forests, for low- land and montane forests, and for mangrove forests. The most important predictors of AGB of a tree were, in decreasing order of importance, its trunk diameter, wood specific gravity, total height, and forest type (dry, moist, or wet). Overestimates prevailed, giving a bias of 0.5-6.5% when errors were averaged across all stands. Our regression models can be used reliably to predict aboveground tree biomass across a broad range of tropical forests. Because they are based on an unprece- dented dataset, these models should improve the quality