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A universal approach to estimate biomass and carbon stock in tropical forests using generic allometric models
Authors:Vieilledent G  Vaudry R  Andriamanohisoa S F D  Rakotonarivo O S  Randrianasolo H Z  Razafindrabe H N  Rakotoarivony C Bidaud  Ebeling J  Rasamoelina M
Institution:CIRAD, UR 105 Biens et Services des Ecosystèmes Forestiers, TA C-105/D, Campus International de Baillarguet, F-34398 Montpellier Cedex 5, France. ghislain.vieilledent@cirad.fr
Abstract:Allometric equations allow aboveground tree biomass and carbon stock to be estimated from tree size. The allometric scaling theory suggests the existence of a universal power-law relationship between tree biomass and tree diameter with a fixed scaling exponent close to 8/3. In addition, generic empirical models, like Chave's or Brown's models, have been proposed for tropical forests in America and Asia. These generic models have been used to estimate forest biomass and carbon worldwide. However, tree allometry depends on environmental and genetic factors that vary from region to region. Consequently, theoretical models that include too few ecological explicative variables or empirical generic models that have been calibrated at particular sites are unlikely to yield accurate tree biomass estimates at other sites. In this study, we based our analysis on a destructive sample of 481 trees in Madagascar spiny dry and moist forests characterized by a high rate of endemism (> 95%). We show that, among the available generic allometric models, Chave's model including diameter, height, and wood specific gravity as explicative variables for a particular forest type (dry, moist, or wet tropical forest) was the only one that gave accurate tree biomass estimates for Madagascar (R2 > 83%, bias < 6%), with estimates comparable to those obtained with regional allometric models. When biomass allometric models are not available for a given forest site, this result shows that a simple height-diameter allometry is needed to accurately estimate biomass and carbon stock from plot inventories.
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